Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

ABSTRACT

A three-dimensional data encoding method includes: calculating coefficient values from pieces of attribute information of three-dimensional points included in point cloud data; quantizing the coefficient values to generate quantization values; and generating a bitstream including the quantization values. The three-dimensional points corresponding to the coefficient values belong to one layer among one or more layers. Each of a predetermined number of layers among the one or more layers is assigned a quantization parameter for the layer. In the quantizing, (i) when a quantization parameter is assigned to a layer to which each of the coefficient values belongs, the coefficient value is quantized using the quantization parameter, and (ii) when the quantization parameter is not assigned to a layer to which each of the coefficient values belongs, the coefficient value is quantized using a quantization parameter assigned to one layer among the predetermined number of the layers.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. continuation application of PCT InternationalPatent Application Number PCT/JP2020/017861 filed on Apr. 24, 2020,claiming the benefit of priority of U.S. Provisional Patent ApplicationNo. 62/838,678 filed on Apr. 25, 2019, the entire contents of which arehereby incorporated by reference.

BACKGROUND 1. Technical Field

The present disclosure relates to a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, and a three-dimensional data decoding device.

2. Description of the Related Art

Devices or services utilizing three-dimensional data are expected tofind their widespread use in a wide range of fields, such as computervision that enables autonomous operations of cars or robots, mapinformation, monitoring, infrastructure inspection, and videodistribution. Three-dimensional data is obtained through various meansincluding a distance sensor such as a rangefinder, as well as a stereocamera and a combination of a plurality of monocular cameras.

Methods of representing three-dimensional data include a method known asa point cloud scheme that represents the shape of a three-dimensionalstructure by a point group in a three-dimensional space. In the pointcloud scheme, the positions and colors of a point group are stored.While point cloud is expected to be a mainstream method of representingthree-dimensional data, a massive amount of data of a point groupnecessitates compression of the amount of three-dimensional data byencoding for accumulation and transmission, as in the case of atwo-dimensional moving picture (examples include MPEG-4 AVC and HEVCstandardized by MPEG).

Meanwhile, point cloud compression is partially supported by, forexample, an open-source library (Point Cloud Library) for pointcloud-related processing.

Furthermore, a technique for searching for and displaying a facilitylocated in the surroundings of the vehicle is known (for example, seeInternational Publication WO 2014/020663).

SUMMARY

There has been a demand for performing encoding appropriately in athree-dimensional data encoding process.

The present disclosure has an object to provide a three-dimensional dataencoding method, a three-dimensional data decoding method, athree-dimensional data encoding device, or a three-dimensional datadecoding device that is capable of performing encoding appropriately.

A three-dimensional data encoding method according to one aspect of thepresent disclosure includes: calculating coefficient values from piecesof attribute information of three-dimensional points included in pointcloud data; quantizing the coefficient values to generate quantizationvalues; and generating a bitstream including the quantization values.The three-dimensional points corresponding to the coefficient valuesbelong to one layer among one or more layers. Each of a predeterminednumber of layers among the one or more layers is assigned a quantizationparameter for the layer. In the quantizing, (i) when a quantizationparameter is assigned to a layer to which each of the coefficient valuesbelongs, the coefficient value is quantized using the quantizationparameter, and (ii) when the quantization parameter is not assigned to alayer to which each of the coefficient values belongs, the coefficientvalue is quantized using a quantization parameter assigned to one layeramong the predetermined number of the layers.

A three-dimensional data encoding method according to another aspect ofthe present disclosure includes: calculating coefficient values frompieces of attribute information of three-dimensional points included inpoint cloud data; quantizing the coefficient values to generatequantization values; and generating a bitstream including thequantization values. Each of the coefficient values belongs to one groupamong groups that is associated with, among three-dimensional spaces, athree-dimensional space to which a three-dimensional point havingattribute information used to calculate the coefficient value belongs.In the quantizing, each of the coefficient values is quantized using aquantization parameter for the one group to which the coefficient valuebelongs.

A three-dimensional data decoding method according to one aspect of thepresent disclosure includes: inverse quantizing quantization values togenerate coefficient values, the quantization values being included in abitstream; and calculating, from the coefficient values, pieces ofattribute information of three-dimensional points included in pointcloud data. The three-dimensional points corresponding to thecoefficient values belong to one layer among one or more layers. Each ofa predetermined number of layers among the one or more layers isassigned a quantization parameter for the layer. In the inversequantizing, (i) when a quantization parameter is assigned to a layer towhich each of the quantization values belongs, the quantization value isinverse quantized using the quantization parameter, and (ii) when thequantization parameter is not assigned to a layer to which each of thequantization values belongs, the quantization value is inverse quantizedusing a quantization parameter assigned to one layer among thepredetermined number of the layers.

A three-dimensional data decoding method according to another aspect ofthe present disclosure includes: inverse quantizing quantization valuesto generate coefficient values, the quantization values being includedin a bitstream; and calculating, from the coefficient values, pieces ofattribute information of three-dimensional points included in pointcloud data. Each of the quantization values belongs to one group amonggroups that is associated with, among three-dimensional spaces, athree-dimensional space to which a three-dimensional point havingattribute information used to calculate the quantization value belongs.Each of a predetermined number of layers among the one or more layers isassigned a quantization parameter for the layer. In the inversequantizing, each of the quantization values is inverse quantized using aquantization parameter for a layer to which the quantization valuebelongs.

The present disclosure provides a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, or a three-dimensional data decoding device thatis capable of performing encoding appropriately.

BRIEF DESCRIPTION OF DRAWINGS

These and other objects, advantages and features of the disclosure willbecome apparent from the following description thereof taken inconjunction with the accompanying drawings that illustrate a specificembodiment of the present disclosure.

FIG. 1 is a diagram showing the structure of encoded three-dimensionaldata according to Embodiment 1;

FIG. 2 is a diagram showing an example of prediction structures amongSPCs that belong to the lowermost layer in a GOS according to Embodiment1;

FIG. 3 is a diagram showing an example of prediction structures amonglayers according to Embodiment 1;

FIG. 4 is a diagram showing an example order of encoding GOSs accordingto Embodiment 1;

FIG. 5 is a diagram showing an example order of encoding GOSs accordingto Embodiment 1;

FIG. 6 is a diagram showing an example of meta information according toEmbodiment 1;

FIG. 7 is a schematic diagram showing three-dimensional data beingtransmitted/received between vehicles according to Embodiment 2;

FIG. 8 is a diagram showing an example of three-dimensional datatransmitted between vehicles according to Embodiment 2;

FIG. 9 is a diagram that illustrates processes of transmittingthree-dimensional data according to Embodiment 3;

FIG. 10 is a diagram showing a structure of a system according toEmbodiment 4;

FIG. 11 is a block diagram of a client device according to Embodiment 4;

FIG. 12 is a block diagram of a server according to Embodiment 4;

FIG. 13 is a flowchart of a three-dimensional data creation processperformed by the client device according to Embodiment 4;

FIG. 14 is a flowchart of a sensor information transmission processperformed by the client device according to Embodiment 4;

FIG. 15 is a flowchart of a three-dimensional data creation processperformed by the server according to Embodiment 4;

FIG. 16 is a flowchart of a three-dimensional map transmission processperformed by the server according to Embodiment 4;

FIG. 17 is a diagram showing a structure of a variation of the systemaccording to Embodiment 4;

FIG. 18 is a diagram showing a structure of the server and clientdevices according to Embodiment 4;

FIG. 19 is a diagram illustrating a configuration of a server and aclient device according to Embodiment 5;

FIG. 20 is a flowchart of a process performed by the client deviceaccording to Embodiment 5;

FIG. 21 is a diagram illustrating a configuration of a sensorinformation collection system according to Embodiment 5;

FIG. 22 is a diagram showing an example of a volume according toEmbodiment 6;

FIG. 23 is a diagram showing an example of an octree representation ofthe volume according to Embodiment 6;

FIG. 24 is a diagram showing an example of bit sequences of the volumeaccording to Embodiment 6;

FIG. 25 is a diagram showing an example of an octree representation of avolume according to Embodiment 6;

FIG. 26 is a diagram showing an example of the volume according toEmbodiment 6;

FIG. 27 is a diagram illustrating an example of three-dimensional pointsaccording to Embodiment 7;

FIG. 28 is a diagram illustrating an example of setting LoDs accordingto Embodiment 7;

FIG. 29 is a diagram illustrating an example of setting LoDs accordingto Embodiment 7;

FIG. 30 is a diagram illustrating an example of attribute information tobe used for predicted values according to Embodiment 7;

FIG. 31 is a diagram illustrating examples of exponential-Golomb codesaccording to Embodiment 7;

FIG. 32 is a diagram indicating a process on exponential-Golomb codesaccording to Embodiment 7;

FIG. 33 is a diagram indicating an example of a syntax in attributeheader according to Embodiment 7;

FIG. 34 is a diagram indicating an example of a syntax in attribute dataaccording to Embodiment 7;

FIG. 35 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 7;

FIG. 36 is a flowchart of an attribute information encoding processaccording to Embodiment 7;

FIG. 37 is a diagram indicating processing on exponential-Golomb codesaccording to Embodiment 7;

FIG. 38 is a diagram indicating an example of a reverse lookup tableindicating relationships between remaining codes and the values thereofaccording to Embodiment 7;

FIG. 39 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 7;

FIG. 40 is a flowchart of an attribute information decoding processaccording to Embodiment 7;

FIG. 41 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 7;

FIG. 42 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 7;

FIG. 43 is a diagram for describing the encoding of the attributeinformation by using a RAHT according to Embodiment 8;

FIG. 44 is a diagram showing an example of setting a quantization scalefor each hierarchy according to Embodiment 8;

FIG. 45 is a diagram showing an example of a first code sequence and asecond code sequence according to Embodiment 8;

FIG. 46 is a diagram showing an example of a truncated unary codeaccording to Embodiment 8;

FIG. 47 is a diagram for describing the inverse Haar conversionaccording to Embodiment 8;

FIG. 48 is a diagram showing a syntax example of the attributeinformation according to Embodiment 8;

FIG. 49 is a diagram showing an example of a coding coefficient andZeroCnt according to Embodiment 8;

FIG. 50 is a flowchart of the three-dimensional data encoding processingaccording to Embodiment 8;

FIG. 51 is a flowchart of the attribute information encoding processingaccording to Embodiment 8;

FIG. 52 is a flowchart of the coding coefficient encoding processingaccording to Embodiment 8;

FIG. 53 is a flowchart of the three-dimensional data decoding processingaccording to Embodiment 8;

FIG. 54 is a flowchart of the attribute information decoding processingaccording to Embodiment 8;

FIG. 55 is a flowchart the coding coefficient decoding processingaccording to Embodiment 8;

FIG. 56 is a block diagram of an attribute information encoder accordingto Embodiment 8;

FIG. 57 is a block diagram of an attribute information decoder accordingto Embodiment 8;

FIG. 58 is a diagram showing an example of a first code sequence and asecond code sequence according to a modification of Embodiment 8;

FIG. 59 is a diagram showing a syntax example of the attributeinformation according to the modification of Embodiment 8;

FIG. 60 is a diagram showing an example of a coding coefficient,ZeroCnt, and TotalZeroCnt according to the modification of Embodiment 8;

FIG. 61 is a flowchart of the coding coefficient encoding processingaccording to the modification of Embodiment 8;

FIG. 62 is a flowchart of the coding coefficient decoding processingaccording to the modification of Embodiment 8;

FIG. 63 is a diagram showing a syntax example of the attributeinformation according to the modification of Embodiment 8;

FIG. 64 is a block diagram showing a configuration of athree-dimensional data encoding device according to Embodiment 9;

FIG. 65 is a block diagram showing a configuration of athree-dimensional data decoding device according to Embodiment 9;

FIG. 66 is a diagram showing an example of the setting of LoDs accordingto Embodiment 9;

FIG. 67 is a diagram showing an example of a hierarchical structure ofRAHT according to Embodiment 9;

FIG. 68 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 9;

FIG. 69 is a block diagram of a divider according to Embodiment 9;

FIG. 70 is a block diagram of an attribute information encoder accordingto Embodiment 9;

FIG. 71 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 9;

FIG. 72 is a block diagram of an attribute information decoder accordingto Embodiment 9;

FIG. 73 is a diagram showing an example of the setting of a quantizationparameter in the tile division and the slice division according toEmbodiment 9;

FIG. 74 is a diagram showing an example of the setting of a quantizationparameter according to Embodiment 9;

FIG. 75 is a diagram showing an example of the setting of a quantizationparameter according to Embodiment 9;

FIG. 76 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 9;

FIG. 77 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 9;

FIG. 78 is a diagram showing an example of the setting of a quantizationparameter according to Embodiment 9;

FIG. 79 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 9;

FIG. 80 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 9;

FIG. 81 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 9;

FIG. 82 is a flowchart of an attribute information encoding processaccording to Embodiment 9;

FIG. 83 is a flowchart of a ΔQP determination process according toEmbodiment 9;

FIG. 84 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 9;

FIG. 85 is a flowchart of an attribute information decoding processaccording to Embodiment 9;

FIG. 86 is a block diagram of an attribute information encoder accordingto Embodiment 9;

FIG. 87 is a block diagram of an attribute information decoder accordingto Embodiment 9;

FIG. 88 is a diagram showing an example of the setting of a quantizationparameter according to Embodiment 9;

FIG. 89 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 9;

FIG. 90 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 9;

FIG. 91 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 9;

FIG. 92 is a flowchart of an attribute information encoding processaccording to Embodiment 9;

FIG. 93 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 9;

FIG. 94 is a flowchart of an attribute information decoding processaccording to Embodiment 9;

FIG. 95 is a block diagram of an attribute information encoder accordingto Embodiment 9;

FIG. 96 is a block diagram of an attribute information decoder accordingto Embodiment 9;

FIG. 97 is a diagram showing a syntax example of an attributeinformation header according to Embodiment 9;

FIG. 98 is a graph showing a relationship between bitrate of encoding ofa bitstream and time according to Embodiment 10;

FIG. 99 is a diagram showing a hierarchical structure of athree-dimensional point cloud and the number of three-dimensional pointsbelonging to each layer according to Embodiment 10;

FIG. 100 is a diagram showing a first example of the classification of athree-dimensional point cloud in one layer into sub-layers eachincluding a specified number of three-dimensional points according toEmbodiment 10;

FIG. 101 is a diagram showing a second example of the classification ofa three-dimensional point cloud in one layer into sub-layers eachincluding the same number of three-dimensional points according toEmbodiment 10;

FIG. 102 shows a syntax example of a header of attribute information inthe second example according to Embodiment 10;

FIG. 103 shows another syntax example of attribute information in thesecond example according to Embodiment 10;

FIG. 104 is a diagram showing a third example of the classification of athree-dimensional point cloud in one layer into a different number ofsub-layers than predetermined according to Embodiment 10;

FIG. 105 shows a syntax example of a header of attribute information inthe third example according to Embodiment 10;

FIG. 106 shows another syntax example of a header of attributeinformation in the third example according to Embodiment 10;

FIG. 107 is a diagram showing a fourth example of the classification ofa three-dimensional point cloud in one layer into sub-layers eachincluding a specified ratio (percentage) of the three-dimensional pointsaccording to Embodiment 10;

FIG. 108 shows an example of a syntax of a header of attributeinformation in the fourth example according to Embodiment 10;

FIG. 109 is a diagram showing a fifth example of the classification of athree-dimensional point cloud in one layer into sub-layers based onMorton indices according to Embodiment 10;

FIG. 110 is a syntax example of a header of attribute information in thefifth example according to Embodiment 10;

FIG. 111 is a diagram showing a sixth example of the classification of athree-dimensional point cloud in one layer into sub-layers based onMorton indices according to Embodiment 10;

FIG. 112 is a diagram showing the sixth example of the classification ofa three-dimensional point cloud in one layer into sub-layers based onMorton indices according to Embodiment 10;

FIG. 113 is a diagram showing a seventh example of the classification ofa three-dimensional point cloud in one layer into sub-layers using aresidual or Delta value according to Embodiment 10;

FIG. 114 is a diagram showing an arrangement of three-dimensional pointsarranged in a two-dimensional Morton order according to Embodiment 10;

FIG. 115 shows a syntax example of a header of attribute information inthe seventh example according to Embodiment 10;

FIG. 116 shows a syntax example of a bitstream of a residual accordingto Embodiment 10;

FIG. 117 shows a formula for calculating an encoding cost (Encodingcost) according to Embodiment 10;

FIG. 118 is a graph showing a relationship between bits per point (BPP)and time according to Embodiment 10;

FIG. 119 is a diagram showing that a QP value applied to the encoding ofattribute information is set for each sub-layer according to Embodiment10;

FIG. 120 is a diagram showing an eighth example of the classification ofa three-dimensional point cloud into sub-layers using the Morton codeaccording to Embodiment 10;

FIG. 121 is a syntax example of a header of attribute information in theeighth example according to Embodiment 10;

FIG. 122 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 10; and

FIG. 123 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 10.

DETAILED DESCRIPTION OF THE EMBODIMENTS

A three-dimensional data encoding method according to one aspect of thepresent disclosure includes: calculating coefficient values from piecesof attribute information of three-dimensional points included in pointcloud data; quantizing the coefficient values to generate quantizationvalues; and generating a bitstream including the quantization values.The three-dimensional points corresponding to the coefficient valuesbelong to one layer among one or more layers. Each of a predeterminednumber of layers among the one or more layers is assigned a quantizationparameter for the layer. In the quantizing, (i) when a quantizationparameter is assigned to a layer to which each of the coefficient valuesbelongs, the coefficient value is quantized using the quantizationparameter, and (ii) when the quantization parameter is not assigned to alayer to which each of the coefficient values belongs, the coefficientvalue is quantized using a quantization parameter assigned to one layeramong the predetermined number of the layers.

According to the three-dimensional data encoding method, thequantization parameter can be changed for each layer, and therefore theencoding can be properly performed.

For example, the one layer may be a last layer among the predeterminednumber of the layers.

For example, in the quantizing, when a total number of the one or morelayers is less than the predetermined number of the layers, quantizationparameters assigned to the predetermined number of the layers and notcorresponding to the one or more layers need not be used.

For example, the bitstream may include first information indicating areference quantization parameter, and pieces of second information forcalculating quantization parameters for the one or more layers from thereference quantization parameter.

According to the three-dimensional data encoding method, since firstinformation indicating a reference quantization parameter and aplurality of pieces of second information for calculating a plurality ofquantization parameters from the reference quantization parameter areencoded, the encoding efficiency can be improved.

A three-dimensional data encoding method according to another aspect ofthe present disclosure includes: calculating coefficient values frompieces of attribute information of three-dimensional points included inpoint cloud data; quantizing the coefficient values to generatequantization values; and generating a bitstream including thequantization values. Each of the coefficient values belongs to one groupamong groups that is associated with, among three-dimensional spaces, athree-dimensional space to which a three-dimensional point havingattribute information used to calculate the coefficient value belongs.In the quantizing, each of the coefficient values is quantized using aquantization parameter for the one group to which the coefficient valuebelongs.

According to the three-dimensional data encoding method, thequantization parameter can be changed for each layer, and therefore theencoding can be properly performed.

A three-dimensional data decoding method according to one aspect of thepresent disclosure includes: inverse quantizing quantization values togenerate coefficient values, the quantization values being included in abitstream; and calculating, from the coefficient values, pieces ofattribute information of three-dimensional points included in pointcloud data. The three-dimensional points corresponding to thecoefficient values belong to one layer among one or more layers. Each ofa predetermined number of layers among the one or more layers isassigned a quantization parameter for the layer. In the inversequantizing, (i) when a quantization parameter is assigned to a layer towhich each of the quantization values belongs, the quantization value isinverse quantized using the quantization parameter, and (ii) when thequantization parameter is not assigned to a layer to which each of thequantization values belongs, the quantization value is inverse quantizedusing a quantization parameter assigned to one layer among thepredetermined number of the layers.

According to the three-dimensional data decoding method, thequantization parameter can be changed for each layer, and therefore thedecoding can be properly performed.

For example, the one layer may be a last layer among the predeterminednumber of the layers.

For example, in the inverse quantizing, when a total number of the oneor more layers is less than the predetermined number of the layers,quantization parameters assigned to the predetermined number of thelayers and not corresponding to the one or more layers need not be used.

For example, the bitstream may include first information indicating areference quantization parameter, and pieces of second information forcalculating quantization parameters for the one or more layers from thereference quantization parameter.

The three-dimensional data decoding method is capable of appropriatelydecoding a bitstream whose coding efficiency has been improved, usingfirst information indicating a reference quantization parameter andpieces of second information for calculating quantization parametersfrom the reference quantization parameter.

A three-dimensional data decoding method according to another aspect ofthe present disclosure includes: inverse quantizing quantization valuesto generate coefficient values, the quantization values being includedin a bitstream; and calculating, from the coefficient values, pieces ofattribute information of three-dimensional points included in pointcloud data. Each of the quantization values belongs to one group amonggroups that is associated with, among three-dimensional spaces, athree-dimensional space to which a three-dimensional point havingattribute information used to calculate the quantization value belongs.Each of a predetermined number of layers among the one or more layers isassigned a quantization parameter for the layer. In the inversequantizing, each of the quantization values is inverse quantized using aquantization parameter for a layer to which the quantization valuebelongs.

According to the three-dimensional data decoding method, thequantization parameter can be changed for each layer, and therefore thedecoding can be properly performed.

It is to be noted that these general or specific aspects may beimplemented as a system, a method, an integrated circuit, a computerprogram, or a computer-readable recording medium such as a CD-ROM, ormay be implemented as any combination of a system, a method, anintegrated circuit, a computer program, and a recording medium.

The following describes embodiments with reference to the drawings. Itis to be noted that the following embodiments indicate exemplaryembodiments of the present disclosure. The numerical values, shapes,materials, constituent elements, the arrangement and connection of theconstituent elements, steps, the processing order of the steps, etc.indicated in the following embodiments are mere examples, and thus arenot intended to limit the present disclosure. Of the constituentelements described in the following embodiments, constituent elementsnot recited in any one of the independent claims that indicate thebroadest concepts will be described as optional constituent elements.

Embodiment 1

First, the data structure of encoded three-dimensional data (hereinafteralso referred to as encoded data) according to the present embodimentwill be described. FIG. 1 is a diagram showing the structure of encodedthree-dimensional data according to the present embodiment. FIG. 1 is adiagram showing the structure of encoded three-dimensional dataaccording to the present embodiment.

In the present embodiment, a three-dimensional space is divided intospaces (SPCs), which correspond to pictures in moving picture encoding,and the three-dimensional data is encoded on a SPC-by-SPC basis. EachSPC is further divided into volumes (VLMs), which correspond tomacroblocks, etc. in moving picture encoding, and predictions andtransforms are performed on a VLM-by-VLM basis. Each volume includes aplurality of voxels (VXLs), each being a minimum unit in which positioncoordinates are associated. Note that prediction is a process ofgenerating predictive three-dimensional data analogous to a currentprocessing unit by referring to another processing unit, and encoding adifferential between the predictive three-dimensional data and thecurrent processing unit, as in the case of predictions performed ontwo-dimensional images. Such prediction includes not only spatialprediction in which another prediction unit corresponding to the sametime is referred to, but also temporal prediction in which a predictionunit corresponding to a different time is referred to.

When encoding a three-dimensional space represented by point group datasuch as a point cloud, for example, the three-dimensional data encodingdevice (hereinafter also referred to as the encoding device) encodes thepoints in the point group or points included in the respective voxels ina collective manner, in accordance with a voxel size. Finer voxelsenable a highly-precise representation of the three-dimensional shape ofa point group, while larger voxels enable a rough representation of thethree-dimensional shape of a point group.

Note that the following describes the case where three-dimensional datais a point cloud, but three-dimensional data is not limited to a pointcloud, and thus three-dimensional data of any format may be employed.

Also note that voxels with a hierarchical structure may be used. In sucha case, when the hierarchy includes n levels, whether a sampling pointis included in the n−1th level or its lower levels (the lower levels ofthe n-th level) may be sequentially indicated. For example, when onlythe n-th level is decoded, and the n−1th level or its lower levelsinclude a sampling point, the n-th level can be decoded on theassumption that a sampling point is included at the center of a voxel inthe n-th level.

Also, the encoding device obtains point group data, using, for example,a distance sensor, a stereo camera, a monocular camera, a gyroscopesensor, or an inertial sensor.

As in the case of moving picture encoding, each SPC is classified intoone of at least the three prediction structures that include: intra SPC(I-SPC), which is individually decodable; predictive SPC (P-SPC) capableof only a unidirectional reference; and bidirectional SPC (B-SPC)capable of bidirectional references. Each SPC includes two types of timeinformation: decoding time and display time.

Furthermore, as shown in FIG. 1, a processing unit that includes aplurality of SPCs is a group of spaces (GOS), which is a random accessunit. Also, a processing unit that includes a plurality of GOSs is aworld (WLD).

The spatial region occupied by each world is associated with an absoluteposition on earth, by use of, for example, GPS, or latitude andlongitude information. Such position information is stored asmeta-information. Note that meta-information may be included in encodeddata, or may be transmitted separately from the encoded data.

Also, inside a GOS, all SPCs may be three-dimensionally adjacent to oneanother, or there may be a SPC that is not three-dimensionally adjacentto another SPC.

Note that the following also describes processes such as encoding,decoding, and reference to be performed on three-dimensional dataincluded in processing units such as GOS, SPC, and VLM, simply asperforming encoding/to encode, decoding/to decode, referring to, etc. ona processing unit. Also note that three-dimensional data included in aprocessing unit includes, for example, at least one pair of a spatialposition such as three-dimensional coordinates and an attribute valuesuch as color information.

Next, the prediction structures among SPCs in a GOS will be described. Aplurality of SPCs in the same GOS or a plurality of VLMs in the same SPCoccupy mutually different spaces, while having the same time information(the decoding time and the display time).

A SPC in a GOS that comes first in the decoding order is an I-SPC. GOSscome in two types: closed GOS and open GOS. A closed GOS is a GOS inwhich all SPCs in the GOS are decodable when decoding starts from thefirst I-SPC. Meanwhile, an open GOS is a GOS in which a different GOS isreferred to in one or more SPCs preceding the first I-SPC in the GOS inthe display time, and thus cannot be singly decoded.

Note that in the case of encoded data of map information, for example, aWLD is sometimes decoded in the backward direction, which is opposite tothe encoding order, and thus backward reproduction is difficult whenGOSs are interdependent. In such a case, a closed GOS is basically used.

Each GOS has a layer structure in height direction, and SPCs aresequentially encoded or decoded from SPCs in the bottom layer.

FIG. 2 is a diagram showing an example of prediction structures amongSPCs that belong to the lowermost layer in a GOS. FIG. 3 is a diagramshowing an example of prediction structures among layers.

A GOS includes at least one I-SPC. Of the objects in a three-dimensionalspace, such as a person, an animal, a car, a bicycle, a signal, and abuilding serving as a landmark, a small-sized object is especiallyeffective when encoded as an I-SPC. When decoding a GOS at a lowthroughput or at a high speed, for example, the three-dimensional datadecoding device (hereinafter also referred to as the decoding device)decodes only I-SPC(s) in the GOS.

The encoding device may also change the encoding interval or theappearance frequency of I-SPCs, depending on the degree of sparsenessand denseness of the objects in a WLD.

In the structure shown in FIG. 3, the encoding device or the decodingdevice encodes or decodes a plurality of layers sequentially from thebottom layer (layer 1). This increases the priority of data on theground and its vicinity, which involve a larger amount of information,when, for example, a self-driving car is concerned.

Regarding encoded data used for a drone, for example, encoding ordecoding may be performed sequentially from SPCs in the top layer in aGOS in height direction.

The encoding device or the decoding device may also encode or decode aplurality of layers in a manner that the decoding device can have arough grasp of a GOS first, and then the resolution is graduallyincreased. The encoding device or the decoding device may performencoding or decoding in the order of layers 3, 8, 1, 9 . . . , forexample.

Next, the handling of static objects and dynamic objects will bedescribed.

A three-dimensional space includes scenes or still objects such as abuilding and a road (hereinafter collectively referred to as staticobjects), and objects with motion such as a car and a person(hereinafter collectively referred to as dynamic objects). Objectdetection is separately performed by, for example, extracting keypointsfrom point cloud data, or from video of a camera such as a stereocamera. In this description, an example method of encoding a dynamicobject will be described.

A first method is a method in which a static object and a dynamic objectare encoded without distinction. A second method is a method in which adistinction is made between a static object and a dynamic object on thebasis of identification information.

For example, a GOS is used as an identification unit. In such a case, adistinction is made between a GOS that includes SPCs constituting astatic object and a GOS that includes SPCs constituting a dynamicobject, on the basis of identification information stored in the encodeddata or stored separately from the encoded data.

Alternatively, a SPC may be used as an identification unit. In such acase, a distinction is made between a SPC that includes VLMsconstituting a static object and a SPC that includes VLMs constituting adynamic object, on the basis of the identification information thusdescribed.

Alternatively, a VLM or a VXL may be used as an identification unit. Insuch a case, a distinction is made between a VLM or a VXL that includesa static object and a VLM or a VXL that includes a dynamic object, onthe basis of the identification information thus described.

The encoding device may also encode a dynamic object as at least one VLMor SPC, and may encode a VLM or a SPC including a static object and aSPC including a dynamic object as mutually different GOSs. When the GOSsize is variable depending on the size of a dynamic object, the encodingdevice separately stores the GOS size as meta-information.

The encoding device may also encode a static object and a dynamic objectseparately from each other, and may superimpose the dynamic object ontoa world constituted by static objects. In such a case, the dynamicobject is constituted by at least one SPC, and each SPC is associatedwith at least one SPC constituting the static object onto which the eachSPC is to be superimposed. Note that a dynamic object may be representednot by SPC(s) but by at least one VLM or VXL.

The encoding device may also encode a static object and a dynamic objectas mutually different streams.

The encoding device may also generate a GOS that includes at least oneSPC constituting a dynamic object. The encoding device may further setthe size of a GOS including a dynamic object (GOS M) and the size of aGOS including a static object corresponding to the spatial region ofGOS_M at the same size (such that the same spatial region is occupied).This enables superimposition to be performed on a GOS-by-GOS basis.

SPC(s) included in another encoded GOS may be referred to in a P-SPC ora B-SPC constituting a dynamic object. In the case where the position ofa dynamic object temporally changes, and the same dynamic object isencoded as an object in a GOS corresponding to a different time,referring to SPC(s) across GOSs is effective in terms of compressionrate.

The first method and the second method may be selected in accordancewith the intended use of encoded data. When encoded three-dimensionaldata is used as a map, for example, a dynamic object is desired to beseparated, and thus the encoding device uses the second method.Meanwhile, the encoding device uses the first method when the separationof a dynamic object is not required such as in the case wherethree-dimensional data of an event such as a concert and a sports eventis encoded.

The decoding time and the display time of a GOS or a SPC are storable inencoded data or as meta-information. All static objects may have thesame time information. In such a case, the decoding device may determinethe actual decoding time and display time. Alternatively, a differentvalue may be assigned to each GOS or SPC as the decoding time, and thesame value may be assigned as the display time. Furthermore, as in thecase of the decoder model in moving picture encoding such asHypothetical Reference Decoder (HRD) compliant with HEVC, a model may beemployed that ensures that a decoder can perform decoding without failby having a buffer of a predetermined size and by reading a bitstream ata predetermined bit rate in accordance with the decoding times.

Next, the topology of GOSs in a world will be described. The coordinatesof the three-dimensional space in a world are represented by the threecoordinate axes (x axis, y axis, and z axis) that are orthogonal to oneanother. A predetermined rule set for the encoding order of GOSs enablesencoding to be performed such that spatially adjacent GOSs arecontiguous in the encoded data. In an example shown in FIG. 4, forexample, GOSs in the x and z planes are successively encoded. After thecompletion of encoding all GOSs in certain x and z planes, the value ofthe y axis is updated. Stated differently, the world expands in the yaxis direction as the encoding progresses. The GOS index numbers are setin accordance with the encoding order.

Here, the three-dimensional spaces in the respective worlds arepreviously associated one-to-one with absolute geographical coordinatessuch as GPS coordinates or latitude/longitude coordinates.Alternatively, each three-dimensional space may be represented as aposition relative to a previously set reference position. The directionsof the x axis, the y axis, and the z axis in the three-dimensional spaceare represented by directional vectors that are determined on the basisof the latitudes and the longitudes, etc. Such directional vectors arestored together with the encoded data as meta-information.

GOSs have a fixed size, and the encoding device stores such size asmeta-information. The GOS size may be changed depending on, for example,whether it is an urban area or not, or whether it is inside or outsideof a room. Stated differently, the GOS size may be changed in accordancewith the amount or the attributes of objects with information values.Alternatively, in the same world, the encoding device may adaptivelychange the GOS size or the interval between I-SPCs in GOSs in accordancewith the object density, etc. For example, the encoding device sets theGOS size to smaller and the interval between I-SPCs in GOSs to shorter,as the object density is higher.

In an example shown in FIG. 5, to enable random access with a finergranularity, a GOS with a high object density is partitioned into theregions of the third to tenth GOSs. Note that the seventh to tenth GOSsare located behind the third to sixth GOSs.

Embodiment 2

The present embodiment will describe a method of transmitting/receivingthree-dimensional data between vehicles.

FIG. 7 is a schematic diagram showing three-dimensional data 607 beingtransmitted/received between own vehicle 600 and nearby vehicle 601.

In three-dimensional data that is obtained by a sensor mounted on ownvehicle 600 (e.g., a distance sensor such as a rangefinder, as well as astereo camera and a combination of a plurality of monocular cameras),there appears a region, three-dimensional data of which cannot becreated, due to an obstacle such as nearby vehicle 601, despite thatsuch region is included in sensor detection range 602 of own vehicle 600(such region is hereinafter referred to as occlusion region 604). Also,while the obtainment of three-dimensional data of a larger space enablesa higher accuracy of autonomous operations, a range of sensor detectiononly by own vehicle 600 is limited.

Sensor detection range 602 of own vehicle 600 includes region 603,three-dimensional data of which is obtainable, and occlusion region 604.A range, three-dimensional data of which own vehicle 600 wishes toobtain, includes sensor detection range 602 of own vehicle 600 and otherregions. Sensor detection range 605 of nearby vehicle 601 includesocclusion region 604 and region 606 that is not included in sensordetection range 602 of own vehicle 600.

Nearby vehicle 601 transmits information detected by nearby vehicle 601to own vehicle 600. Own vehicle 600 obtains the information detected bynearby vehicle 601, such as a preceding vehicle, thereby obtainingthree-dimensional data 607 of occlusion region 604 and region 606outside of sensor detection range 602 of own vehicle 600. Own vehicle600 uses the information obtained by nearby vehicle 601 to complementthe three-dimensional data of occlusion region 604 and region 606outside of the sensor detection range.

The usage of three-dimensional data in autonomous operations of avehicle or a robot includes self-location estimation, detection ofsurrounding conditions, or both. For example, for self-locationestimation, three-dimensional data is used that is generated by ownvehicle 600 on the basis of sensor information of own vehicle 600. Fordetection of surrounding conditions, three-dimensional data obtainedfrom nearby vehicle 601 is also used in addition to thethree-dimensional data generated by own vehicle 600.

Nearby vehicle 601 that transmits three-dimensional data 607 to ownvehicle 600 may be determined in accordance with the state of ownvehicle 600. For example, the current nearby vehicle 601 is a precedingvehicle when own vehicle 600 is running straight ahead, an oncomingvehicle when own vehicle 600 is turning right, and a following vehiclewhen own vehicle 600 is rolling backward. Alternatively, the driver ofown vehicle 600 may directly specify nearby vehicle 601 that transmitsthree-dimensional data 607 to own vehicle 600.

Alternatively, own vehicle 600 may search for nearby vehicle 601 havingthree-dimensional data of a region that is included in a space,three-dimensional data of which own vehicle 600 wishes to obtain, andthat own vehicle 600 cannot obtain. The region own vehicle 600 cannotobtain is occlusion region 604, or region 606 outside of sensordetection range 602, etc.

Own vehicle 600 may identify occlusion region 604 on the basis of thesensor information of own vehicle 600. For example, own vehicle 600identifies, as occlusion region 604, a region which is included insensor detection range 602 of own vehicle 600, and three-dimensionaldata of which cannot be created.

The following describes example operations to be performed when avehicle that transmits three-dimensional data 607 is a precedingvehicle. FIG. 8 is a diagram showing an example of three-dimensionaldata to be transmitted in such case.

As FIG. 8 shows, three-dimensional data 607 transmitted from thepreceding vehicle is, for example, a sparse world (SWLD) of a pointcloud. Stated differently, the preceding vehicle createsthree-dimensional data (point cloud) of a WLD from information detectedby a sensor of such preceding vehicle, and extracts data having anamount of features greater than or equal to the threshold from suchthree-dimensional data of the WLD, thereby creating three-dimensionaldata (point cloud) of the SWLD. Subsequently, the preceding vehicletransmits the created three-dimensional data of the SWLD to own vehicle600.

Own vehicle 600 receives the SWLD, and merges the received SWLD with thepoint cloud created by own vehicle 600.

The SWLD to be transmitted includes information on the absolutecoordinates (the position of the SWLD in the coordinates system of athree-dimensional map). The merge is achieved by own vehicle 600overwriting the point cloud generated by own vehicle 600 on the basis ofsuch absolute coordinates.

The SWLD transmitted from nearby vehicle 601 may be: a SWLD of region606 that is outside of sensor detection range 602 of own vehicle 600 andwithin sensor detection range 605 of nearby vehicle 601; or a SWLD ofocclusion region 604 of own vehicle 600; or the SWLDs of the both. Ofthese SWLDs, a SWLD to be transmitted may also be a SWLD of a regionused by nearby vehicle 601 to detect the surrounding conditions.

Nearby vehicle 601 may change the density of a point cloud to transmit,in accordance with the communication available time, during which ownvehicle 600 and nearby vehicle 601 can communicate, and which is basedon the speed difference between these vehicles. For example, when thespeed difference is large and the communication available time is short,nearby vehicle 601 may extract three-dimensional points having a largeamount of features from the SWLD to decrease the density (data amount)of the point cloud.

The detection of the surrounding conditions refers to judging thepresence/absence of persons, vehicles, equipment for roadworks, etc.,identifying their types, and detecting their positions, travellingdirections, traveling speeds, etc.

Own vehicle 600 may obtain braking information of nearby vehicle 601instead of or in addition to three-dimensional data 607 generated bynearby vehicle 601. Here, the braking information of nearby vehicle 601is, for example, information indicating that the accelerator or thebrake of nearby vehicle 601 has been pressed, or the degree of suchpressing.

In the point clouds generated by the vehicles, the three-dimensionalspaces are segmented on a random access unit, in consideration oflow-latency communication between the vehicles. Meanwhile, in athree-dimensional map, etc., which is map data downloaded from theserver, a three-dimensional space is segmented in a larger random accessunit than in the case of inter-vehicle communication.

Data on a region that is likely to be an occlusion region, such as aregion in front of the preceding vehicle and a region behind thefollowing vehicle, is segmented on a finer random access unit aslow-latency data.

Data on a region in front of a vehicle has an increased importance whenon an expressway, and thus each vehicle creates a SWLD of a range with anarrowed viewing angle on a finer random access unit when running on anexpressway.

When the SWLD created by the preceding vehicle for transmission includesa region, the point cloud of which own vehicle 600 can obtain, thepreceding vehicle may remove the point cloud of such region to reducethe amount of data to transmit.

Embodiment 3

The present embodiment describes a method, etc. of transmittingthree-dimensional data to a following vehicle. FIG. 9 is a diagramshowing an exemplary space, three-dimensional data of which is to betransmitted to a following vehicle, etc.

Vehicle 801 transmits, at the time interval of Δt, three-dimensionaldata, such as a point cloud (a point group) included in a rectangularsolid space 802, having width W, height H, and depth D, located ahead ofvehicle 801 and distanced by distance L from vehicle 801, to acloud-based traffic monitoring system that monitors road situations or afollowing vehicle.

When a change has occurred in the three-dimensional data of a space thatis included in space 802 already transmitted in the past, due to avehicle or a person entering space 802 from outside, for example,vehicle 801 also transmits three-dimensional data of the space in whichsuch change has occurred.

Although FIG. 9 illustrates an example in which space 802 has arectangular solid shape, space 802 is not necessarily a rectangularsolid so long as space 802 includes a space on the forward road that ishidden from view of a following vehicle.

Distance L may be set to a distance that allows the following vehiclehaving received the three-dimensional data to stop safely. For example,set as distance L is the sum of; a distance traveled by the followingvehicle while receiving the three-dimensional data; a distance traveledby the following vehicle until the following vehicle starts speedreduction in accordance with the received data; and a distance requiredby the following vehicle to stop safely after starting speed reduction.These distances vary in accordance with the speed, and thus distance Lmay vary in accordance with speed V of the vehicle, just like L=a×V+b (aand b are constants).

Width W is set to a value that is at least greater than the width of thelane on which vehicle 801 is traveling. Width W may also be set to asize that includes an adjacent space such as right and left lanes and aside strip.

Depth D may have a fixed value, but may vary in accordance with speed Vof the vehicle, just like D=c×V+d (c and d are constants). Also, D thatis set to satisfy D>V×Δt enables the overlap of a space to betransmitted and a space transmitted in the past. This enables vehicle801 to transmit a space on the traveling road to the following vehicle,etc. completely and more reliably.

As described above, vehicle 801 transmits three-dimensional data of alimited space that is useful to the following vehicle, therebyeffectively reducing the amount of the three-dimensional data to betransmitted and achieving low-latency, low-cost communication.

Embodiment 4

In Embodiment 3, an example is described in which a client device of avehicle or the like transmits three-dimensional data to another vehicleor a server such as a cloud-based traffic monitoring system. In thepresent embodiment, a client device transmits sensor informationobtained through a sensor to a server or a client device.

A structure of a system according to the present embodiment will firstbe described. FIG. 10 is a diagram showing the structure of atransmission/reception system of a three-dimensional map and sensorinformation according to the present embodiment. This system includesserver 901, and client devices 902A and 902B. Note that client devices902A and 902B are also referred to as client device 902 when noparticular distinction is made therebetween.

Client device 902 is, for example, a vehicle-mounted device equipped ina mobile object such as a vehicle. Server 901 is, for example, acloud-based traffic monitoring system, and is capable of communicatingwith the plurality of client devices 902.

Server 901 transmits the three-dimensional map formed by a point cloudto client device 902. Note that a structure of the three-dimensional mapis not limited to a point cloud, and may also be another structureexpressing three-dimensional data such as a mesh structure.

Client device 902 transmits the sensor information obtained by clientdevice 902 to server 901. The sensor information includes, for example,at least one of information obtained by LIDAR, a visible light image, aninfrared image, a depth image, sensor position information, or sensorspeed information.

The data to be transmitted and received between server 901 and clientdevice 902 may be compressed in order to reduce data volume, and mayalso be transmitted uncompressed in order to maintain data precision.When compressing the data, it is possible to use a three-dimensionalcompression method on the point cloud based on, for example, an octreestructure. It is possible to use a two-dimensional image compressionmethod on the visible light image, the infrared image, and the depthimage. The two-dimensional image compression method is, for example,MPEG-4 AVC or HEVC standardized by MPEG.

Server 901 transmits the three-dimensional map managed by server 901 toclient device 902 in response to a transmission request for thethree-dimensional map from client device 902. Note that server 901 mayalso transmit the three-dimensional map without waiting for thetransmission request for the three-dimensional map from client device902. For example, server 901 may broadcast the three-dimensional map toat least one client device 902 located in a predetermined space. Server901 may also transmit the three-dimensional map suited to a position ofclient device 902 at fixed time intervals to client device 902 that hasreceived the transmission request once. Server 901 may also transmit thethree-dimensional map managed by server 901 to client device 902 everytime the three-dimensional map is updated.

Client device 902 sends the transmission request for thethree-dimensional map to server 901. For example, when client device 902wants to perform the self-location estimation during traveling, clientdevice 902 transmits the transmission request for the three-dimensionalmap to server 901.

Note that in the following cases, client device 902 may send thetransmission request for the three-dimensional map to server 901. Clientdevice 902 may send the transmission request for the three-dimensionalmap to server 901 when the three-dimensional map stored by client device902 is old. For example, client device 902 may send the transmissionrequest for the three-dimensional map to server 901 when a fixed periodhas passed since the three-dimensional map is obtained by client device902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 before a fixed time when clientdevice 902 exits a space shown in the three-dimensional map stored byclient device 902. For example, client device 902 may send thetransmission request for the three-dimensional map to server 901 whenclient device 902 is located within a predetermined distance from aboundary of the space shown in the three-dimensional map stored byclient device 902. When a movement path and a movement speed of clientdevice 902 are understood, a time when client device 902 exits the spaceshown in the three-dimensional map stored by client device 902 may bepredicted based on the movement path and the movement speed of clientdevice 902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 when an error during alignment ofthe three-dimensional data and the three-dimensional map created fromthe sensor information by client device 902 is at least at a fixedlevel.

Client device 902 transmits the sensor information to server 901 inresponse to a transmission request for the sensor information fromserver 901. Note that client device 902 may transmit the sensorinformation to server 901 without waiting for the transmission requestfor the sensor information from server 901. For example, client device902 may periodically transmit the sensor information during a fixedperiod when client device 902 has received the transmission request forthe sensor information from server 901 once. Client device 902 maydetermine that there is a possibility of a change in thethree-dimensional map of a surrounding area of client device 902 havingoccurred, and transmit this information and the sensor information toserver 901, when the error during alignment of the three-dimensionaldata created by client device 902 based on the sensor information andthe three-dimensional map obtained from server 901 is at least at thefixed level.

Server 901 sends a transmission request for the sensor information toclient device 902. For example, server 901 receives positioninformation, such as GPS information, about client device 902 fromclient device 902. Server 901 sends the transmission request for thesensor information to client device 902 in order to generate a newthree-dimensional map, when it is determined that client device 902 isapproaching a space in which the three-dimensional map managed by server901 contains little information, based on the position information aboutclient device 902. Server 901 may also send the transmission request forthe sensor information, when wanting to (i) update the three-dimensionalmap, (ii) check road conditions during snowfall, a disaster, or thelike, or (iii) check traffic congestion conditions, accident/incidentconditions, or the like.

Client device 902 may set an amount of data of the sensor information tobe transmitted to server 901 in accordance with communication conditionsor bandwidth during reception of the transmission request for the sensorinformation to be received from server 901. Setting the amount of dataof the sensor information to be transmitted to server 901 is, forexample, increasing/reducing the data itself or appropriately selectinga compression method.

FIG. 11 is a block diagram showing an example structure of client device902. Client device 902 receives the three-dimensional map formed by apoint cloud and the like from server 901, and estimates a self-locationof client device 902 using the three-dimensional map created based onthe sensor information of client device 902. Client device 902 transmitsthe obtained sensor information to server 901.

Client device 902 includes data receiver 1011, communication unit 1012,reception controller 1013, format converter 1014, sensors 1015,three-dimensional data creator 1016, three-dimensional image processor1017, three-dimensional data storage 1018, format converter 1019,communication unit 1020, transmission controller 1021, and datatransmitter 1022.

Data receiver 1011 receives three-dimensional map 1031 from server 901.Three-dimensional map 1031 is data that includes a point cloud such as aWLD or a SWLD. Three-dimensional map 1031 may include compressed data oruncompressed data.

Communication unit 1012 communicates with server 901 and transmits adata transmission request (e.g., transmission request forthree-dimensional map) to server 901.

Reception controller 1013 exchanges information, such as information onsupported formats, with a communications partner via communication unit1012 to establish communication with the communications partner.

Format converter 1014 performs a format conversion and the like onthree-dimensional map 1031 received by data receiver 1011 to generatethree-dimensional map 1032. Format converter 1014 also performs adecompression or decoding process when three-dimensional map 1031 iscompressed or encoded. Note that format converter 1014 does not performthe decompression or decoding process when three-dimensional map 1031 isuncompressed data.

Sensors 815 are a group of sensors, such as LIDARs, visible lightcameras, infrared cameras, or depth sensors that obtain informationabout the outside of a vehicle equipped with client device 902, andgenerate sensor information 1033. Sensor information 1033 is, forexample, three-dimensional data such as a point cloud (point group data)when sensors 1015 are laser sensors such as LIDARs. Note that a singlesensor may serve as sensors 1015.

Three-dimensional data creator 1016 generates three-dimensional data1034 of a surrounding area of the own vehicle based on sensorinformation 1033. For example, three-dimensional data creator 1016generates point cloud data with color information on the surroundingarea of the own vehicle using information obtained by LIDAR and visiblelight video obtained by a visible light camera.

Three-dimensional image processor 1017 performs a self-locationestimation process and the like of the own vehicle, using (i) thereceived three-dimensional map 1032 such as a point cloud, and (ii)three-dimensional data 1034 of the surrounding area of the own vehiclegenerated using sensor information 1033. Note that three-dimensionalimage processor 1017 may generate three-dimensional data 1035 about thesurroundings of the own vehicle by merging three-dimensional map 1032and three-dimensional data 1034, and may perform the self-locationestimation process using the created three-dimensional data 1035.

Three-dimensional data storage 1018 stores three-dimensional map 1032,three-dimensional data 1034, three-dimensional data 1035, and the like.

Format converter 1019 generates sensor information 1037 by convertingsensor information 1033 to a format supported by a receiver end. Notethat format converter 1019 may reduce the amount of data by compressingor encoding sensor information 1037. Format converter 1019 may omit thisprocess when format conversion is not necessary. Format converter 1019may also control the amount of data to be transmitted in accordance witha specified transmission range.

Communication unit 1020 communicates with server 901 and receives a datatransmission request (transmission request for sensor information) andthe like from server 901.

Transmission controller 1021 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1020 to establish communication with the communications partner.

Data transmitter 1022 transmits sensor information 1037 to server 901.Sensor information 1037 includes, for example, information obtainedthrough sensors 1015, such as information obtained by LIDAR, a luminanceimage obtained by a visible light camera, an infrared image obtained byan infrared camera, a depth image obtained by a depth sensor, sensorposition information, and sensor speed information.

A structure of server 901 will be described next. FIG. 12 is a blockdiagram showing an example structure of server 901. Server 901 transmitssensor information from client device 902 and creates three-dimensionaldata based on the received sensor information. Server 901 updates thethree-dimensional map managed by server 901 using the createdthree-dimensional data. Server 901 transmits the updatedthree-dimensional map to client device 902 in response to a transmissionrequest for the three-dimensional map from client device 902.

Server 901 includes data receiver 1111, communication unit 1112,reception controller 1113, format converter 1114, three-dimensional datacreator 1116, three-dimensional data merger 1117, three-dimensional datastorage 1118, format converter 1119, communication unit 1120,transmission controller 1121, and data transmitter 1122.

Data receiver 1111 receives sensor information 1037 from client device902. Sensor information 1037 includes, for example, information obtainedby LIDAR, a luminance image obtained by a visible light camera, aninfrared image obtained by an infrared camera, a depth image obtained bya depth sensor, sensor position information, sensor speed information,and the like.

Communication unit 1112 communicates with client device 902 andtransmits a data transmission request (e.g., transmission request forsensor information) and the like to client device 902.

Reception controller 1113 exchanges information, such as information onsupported formats, with a communications partner via communication unit1112 to establish communication with the communications partner.

Format converter 1114 generates sensor information 1132 by performing adecompression or decoding process when the received sensor information1037 is compressed or encoded. Note that format converter 1114 does notperform the decompression or decoding process when sensor information1037 is uncompressed data.

Three-dimensional data creator 1116 generates three-dimensional data1134 of a surrounding area of client device 902 based on sensorinformation 1132. For example, three-dimensional data creator 1116generates point cloud data with color information on the surroundingarea of client device 902 using information obtained by LIDAR andvisible light video obtained by a visible light camera.

Three-dimensional data merger 1117 updates three-dimensional map 1135 bymerging three-dimensional data 1134 created based on sensor information1132 with three-dimensional map 1135 managed by server 901.

Three-dimensional data storage 1118 stores three-dimensional map 1135and the like.

Format converter 1119 generates three-dimensional map 1031 by convertingthree-dimensional map 1135 to a format supported by the receiver end.Note that format converter 1119 may reduce the amount of data bycompressing or encoding three-dimensional map 1135. Format converter1119 may omit this process when format conversion is not necessary.Format converter 1119 may also control the amount of data to betransmitted in accordance with a specified transmission range.

Communication unit 1120 communicates with client device 902 and receivesa data transmission request (transmission request for three-dimensionalmap) and the like from client device 902.

Transmission controller 1121 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1120 to establish communication with the communications partner.

Data transmitter 1122 transmits three-dimensional map 1031 to clientdevice 902. Three-dimensional map 1031 is data that includes a pointcloud such as a WLD or a SWLD. Three-dimensional map 1031 may includeone of compressed data and uncompressed data.

An operational flow of client device 902 will be described next. FIG. 13is a flowchart of an operation when client device 902 obtains thethree-dimensional map.

Client device 902 first requests server 901 to transmit thethree-dimensional map (point cloud, etc.) (S1001). At this point, byalso transmitting the position information about client device 902obtained through GPS and the like, client device 902 may also requestserver 901 to transmit a three-dimensional map relating to this positioninformation.

Client device 902 next receives the three-dimensional map from server901 (S1002). When the received three-dimensional map is compressed data,client device 902 decodes the received three-dimensional map andgenerates an uncompressed three-dimensional map (S1003).

Client device 902 next creates three-dimensional data 1034 of thesurrounding area of client device 902 using sensor information 1033obtained by sensors 1015 (S1004). Client device 902 next estimates theself-location of client device 902 using three-dimensional map 1032received from server 901 and three-dimensional data 1034 created usingsensor information 1033 (S1005).

FIG. 14 is a flowchart of an operation when client device 902 transmitsthe sensor information. Client device 902 first receives a transmissionrequest for the sensor information from server 901 (S1011). Clientdevice 902 that has received the transmission request transmits sensorinformation 1037 to server 901 (S1012). Note that client device 902 maygenerate sensor information 1037 by compressing each piece ofinformation using a compression method suited to each piece ofinformation, when sensor information 1033 includes a plurality of piecesof information obtained by sensors 1015.

An operational flow of server 901 will be described next. FIG. 15 is aflowchart of an operation when server 901 obtains the sensorinformation. Server 901 first requests client device 902 to transmit thesensor information (S1021). Server 901 next receives sensor information1037 transmitted from client device 902 in accordance with the request(S1022). Server 901 next creates three-dimensional data 1134 using thereceived sensor information 1037 (S1023). Server 901 next reflects thecreated three-dimensional data 1134 in three-dimensional map 1135(S1024).

FIG. 16 is a flowchart of an operation when server 901 transmits thethree-dimensional map. Server 901 first receives a transmission requestfor the three-dimensional map from client device 902 (S1031). Server 901that has received the transmission request for the three-dimensional maptransmits the three-dimensional map to client device 902 (S1032). Atthis point, server 901 may extract a three-dimensional map of a vicinityof client device 902 along with the position information about clientdevice 902, and transmit the extracted three-dimensional map. Server 901may compress the three-dimensional map formed by a point cloud using,for example, an octree structure compression method, and transmit thecompressed three-dimensional map.

The following describes variations of the present embodiment.

Server 901 creates three-dimensional data 1134 of a vicinity of aposition of client device 902 using sensor information 1037 receivedfrom client device 902. Server 901 next calculates a difference betweenthree-dimensional data 1134 and three-dimensional map 1135, by matchingthe created three-dimensional data 1134 with three-dimensional map 1135of the same area managed by server 901. Server 901 determines that atype of anomaly has occurred in the surrounding area of client device902, when the difference is greater than or equal to a predeterminedthreshold. For example, it is conceivable that a large difference occursbetween three-dimensional map 1135 managed by server 901 andthree-dimensional data 1134 created based on sensor information 1037,when land subsidence and the like occurs due to a natural disaster suchas an earthquake.

Sensor information 1037 may include information indicating at least oneof a sensor type, a sensor performance, and a sensor model number.Sensor information 1037 may also be appended with a class ID and thelike in accordance with the sensor performance. For example, when sensorinformation 1037 is obtained by LIDAR, it is conceivable to assignidentifiers to the sensor performance. A sensor capable of obtaininginformation with precision in units of several millimeters is class 1, asensor capable of obtaining information with precision in units ofseveral centimeters is class 2, and a sensor capable of obtaininginformation with precision in units of several meters is class 3. Server901 may estimate sensor performance information and the like from amodel number of client device 902. For example, when client device 902is equipped in a vehicle, server 901 may determine sensor specificationinformation from a type of the vehicle. In this case, server 901 mayobtain information on the type of the vehicle in advance, and theinformation may also be included in the sensor information. Server 901may change a degree of correction with respect to three-dimensional data1134 created using sensor information 1037, using the obtained sensorinformation 1037. For example, when the sensor performance is high inprecision (class 1), server 901 does not correct three-dimensional data1134. When the sensor performance is low in precision (class 3), server901 corrects three-dimensional data 1134 in accordance with theprecision of the sensor. For example, server 901 increases the degree(intensity) of correction with a decrease in the precision of thesensor.

Server 901 may simultaneously send the transmission request for thesensor information to the plurality of client devices 902 in a certainspace. Server 901 does not need to use all of the sensor information forcreating three-dimensional data 1134 and may, for example, select sensorinformation to be used in accordance with the sensor performance, whenhaving received a plurality of pieces of sensor information from theplurality of client devices 902. For example, when updatingthree-dimensional map 1135, server 901 may select high-precision sensorinformation (class 1) from among the received plurality of pieces ofsensor information, and create three-dimensional data 1134 using theselected sensor information.

Server 901 is not limited to only being a server such as a cloud-basedtraffic monitoring system, and may also be another (vehicle-mounted)client device. FIG. 17 is a diagram of a system structure in this case.

For example, client device 902C sends a transmission request for sensorinformation to client device 902A located nearby, and obtains the sensorinformation from client device 902A. Client device 902C then createsthree-dimensional data using the obtained sensor information of clientdevice 902A, and updates a three-dimensional map of client device 902C.This enables client device 902C to generate a three-dimensional map of aspace that can be obtained from client device 902A, and fully utilizethe performance of client device 902C. For example, such a case isconceivable when client device 902C has high performance.

In this case, client device 902A that has provided the sensorinformation is given rights to obtain the high-precisionthree-dimensional map generated by client device 902C. Client device902A receives the high-precision three-dimensional map from clientdevice 902C in accordance with these rights.

Server 901 may send the transmission request for the sensor informationto the plurality of client devices 902 (client device 902A and clientdevice 902B) located nearby client device 902C. When a sensor of clientdevice 902A or client device 902B has high performance, client device902C is capable of creating the three-dimensional data using the sensorinformation obtained by this high-performance sensor.

FIG. 18 is a block diagram showing a functionality structure of server901 and client device 902. Server 901 includes, for example,three-dimensional map compression/decoding processor 1201 thatcompresses and decodes the three-dimensional map and sensor informationcompression/decoding processor 1202 that compresses and decodes thesensor information.

Client device 902 includes three-dimensional map decoding processor 1211and sensor information compression processor 1212. Three-dimensional mapdecoding processor 1211 receives encoded data of the compressedthree-dimensional map, decodes the encoded data, and obtains thethree-dimensional map. Sensor information compression processor 1212compresses the sensor information itself instead of thethree-dimensional data created using the obtained sensor information,and transmits the encoded data of the compressed sensor information toserver 901. With this structure, client device 902 does not need tointernally store a processor that performs a process for compressing thethree-dimensional data of the three-dimensional map (point cloud, etc.),as long as client device 902 internally stores a processor that performsa process for decoding the three-dimensional map (point cloud, etc.).This makes it possible to limit costs, power consumption, and the likeof client device 902.

As stated above, client device 902 according to the present embodimentis equipped in the mobile object, and creates three-dimensional data1034 of a surrounding area of the mobile object using sensor information1033 that is obtained through sensor 1015 equipped in the mobile objectand indicates a surrounding condition of the mobile object. Clientdevice 902 estimates a self-location of the mobile object using thecreated three-dimensional data 1034. Client device 902 transmits theobtained sensor information 1033 to server 901 or another mobile object.

This enables client device 902 to transmit sensor information 1033 toserver 901 or the like. This makes it possible to further reduce theamount of transmission data compared to when transmitting thethree-dimensional data. Since there is no need for client device 902 toperform processes such as compressing or encoding the three-dimensionaldata, it is possible to reduce the processing amount of client device902. As such, client device 902 is capable of reducing the amount ofdata to be transmitted or simplifying the structure of the device.

Client device 902 further transmits the transmission request for thethree-dimensional map to server 901 and receives three-dimensional map1031 from server 901. In the estimating of the self-location, clientdevice 902 estimates the self-location using three-dimensional data 1034and three-dimensional map 1032.

Sensor information 1034 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1033 includes information that indicates aperformance of the sensor.

Client device 902 encodes or compresses sensor information 1033, and inthe transmitting of the sensor information, transmits sensor information1037 that has been encoded or compressed to server 901 or another mobileobject 902. This enables client device 902 to reduce the amount of datato be transmitted.

For example, client device 902 includes a processor and memory. Theprocessor performs the above processes using the memory.

Server 901 according to the present embodiment is capable ofcommunicating with client device 902 equipped in the mobile object, andreceives sensor information 1037 that is obtained through sensor 1015equipped in the mobile object and indicates a surrounding condition ofthe mobile object. Server 901 creates three-dimensional data 1134 of asurrounding area of the mobile object using the received sensorinformation 1037.

With this, server 901 creates three-dimensional data 1134 using sensorinformation 1037 transmitted from client device 902. This makes itpossible to further reduce the amount of transmission data compared towhen client device 902 transmits the three-dimensional data. Since thereis no need for client device 902 to perform processes such ascompressing or encoding the three-dimensional data, it is possible toreduce the processing amount of client device 902. As such, server 901is capable of reducing the amount of data to be transmitted orsimplifying the structure of the device.

Server 901 further transmits a transmission request for the sensorinformation to client device 902.

Server 901 further updates three-dimensional map 1135 using the createdthree-dimensional data 1134, and transmits three-dimensional map 1135 toclient device 902 in response to the transmission request forthree-dimensional map 1135 from client device 902.

Sensor information 1037 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1037 includes information that indicates aperformance of the sensor.

Server 901 further corrects the three-dimensional data in accordancewith the performance of the sensor. This enables the three-dimensionaldata creation method to improve the quality of the three-dimensionaldata.

In the receiving of the sensor information, server 901 receives aplurality of pieces of sensor information 1037 received from a pluralityof client devices 902, and selects sensor information 1037 to be used inthe creating of three-dimensional data 1134, based on a plurality ofpieces of information that each indicates the performance of the sensorincluded in the plurality of pieces of sensor information 1037. Thisenables server 901 to improve the quality of three-dimensional data1134.

Server 901 decodes or decompresses the received sensor information 1037,and creates three-dimensional data 1134 using sensor information 1132that has been decoded or decompressed. This enables server 901 to reducethe amount of data to be transmitted.

For example, server 901 includes a processor and memory. The processorperforms the above processes using the memory.

Embodiment 5

In the present embodiment, a variation of Embodiment 4 will bedescribed. FIG. 19 is a diagram illustrating a configuration of a systemaccording to the present embodiment. The system illustrated in FIG. 19includes server 2001, client device 2002A, and client device 2002B.

Client device 2002A and client device 2002B are each provided in amobile object such as a vehicle, and transmit sensor information toserver 2001. Server 2001 transmits a three-dimensional map (a pointcloud) to client device 2002A and client device 2002B.

Client device 2002A includes sensor information obtainer 2011, storage2012, and data transmission possibility determiner 2013. It should benoted that client device 2002B has the same configuration. Additionally,when client device 2002A and client device 2002B are not particularlydistinguished below, client device 2002A and client device 2002B arealso referred to as client device 2002.

FIG. 20 is a flowchart illustrating operation of client device 2002according to the present embodiment.

Sensor information obtainer 2011 obtains a variety of sensor informationusing sensors (a group of sensors) provided in a mobile object. In otherwords, sensor information obtainer 2011 obtains sensor informationobtained by the sensors (the group of sensors) provided in the mobileobject and indicating a surrounding state of the mobile object. Sensorinformation obtainer 2011 also stores the obtained sensor informationinto storage 2012. This sensor information includes at least one ofinformation obtained by LiDAR, a visible light image, an infrared image,or a depth image. Additionally, the sensor information may include atleast one of sensor position information, speed information, obtainmenttime information, or obtainment location information. Sensor positioninformation indicates a position of a sensor that has obtained sensorinformation. Speed information indicates a speed of the mobile objectwhen a sensor obtained sensor information. Obtainment time informationindicates a time when a sensor obtained sensor information. Obtainmentlocation information indicates a position of the mobile object or asensor when the sensor obtained sensor information.

Next, data transmission possibility determiner 2013 determines whetherthe mobile object (client device 2002) is in an environment in which themobile object can transmit sensor information to server 2001 (S2002).For example, data transmission possibility determiner 2013 may specify alocation and a time at which client device 2002 is present using GPSinformation etc., and may determine whether data can be transmitted.Additionally, data transmission possibility determiner 2013 maydetermine whether data can be transmitted, depending on whether it ispossible to connect to a specific access point.

When client device 2002 determines that the mobile object is in theenvironment in which the mobile object can transmit the sensorinformation to server 2001 (YES in S2002), client device 2002 transmitsthe sensor information to server 2001 (S2003). In other words, whenclient device 2002 becomes capable of transmitting sensor information toserver 2001, client device 2002 transmits the sensor information held byclient device 2002 to server 2001. For example, an access point thatenables high-speed communication using millimeter waves is provided inan intersection or the like. When client device 2002 enters theintersection, client device 2002 transmits the sensor information heldby client device 2002 to server 2001 at high speed using themillimeter-wave communication.

Next, client device 2002 deletes from storage 2012 the sensorinformation that has been transmitted to server 2001 (S2004). It shouldbe noted that when sensor information that has not been transmitted toserver 2001 meets predetermined conditions, client device 2002 maydelete the sensor information. For example, when an obtainment time ofsensor information held by client device 2002 precedes a current time bya certain time, client device 2002 may delete the sensor informationfrom storage 2012. In other words, when a difference between the currenttime and a time when a sensor obtained sensor information exceeds apredetermined time, client device 2002 may delete the sensor informationfrom storage 2012. Besides, when an obtainment location of sensorinformation held by client device 2002 is separated from a currentlocation by a certain distance, client device 2002 may delete the sensorinformation from storage 2012. In other words, when a difference betweena current position of the mobile object or a sensor and a position ofthe mobile object or the sensor when the sensor obtained sensorinformation exceeds a predetermined distance, client device 2002 maydelete the sensor information from storage 2012. Accordingly, it ispossible to reduce the capacity of storage 2012 of client device 2002.

When client device 2002 does not finish obtaining sensor information (NOin S2005), client device 2002 performs step S2001 and the subsequentsteps again. Further, when client device 2002 finishes obtaining sensorinformation (YES in S2005), client device 2002 completes the process.

Client device 2002 may select sensor information to be transmitted toserver 2001, in accordance with communication conditions. For example,when high-speed communication is available, client device 2002preferentially transmits sensor information (e.g., information obtainedby LiDAR) of which the data size held in storage 2012 is large.Additionally, when high-speed communication is not readily available,client device 2002 transmits sensor information (e.g., a visible lightimage) which has high priority and of which the data size held instorage 2012 is small. Accordingly, client device 2002 can efficientlytransmit sensor information held in storage 2012, in accordance withnetwork conditions

Client device 2002 may obtain, from server 2001, time informationindicating a current time and location information indicating a currentlocation. Moreover, client device 2002 may determine an obtainment timeand an obtainment location of sensor information based on the obtainedtime information and location information. In other words, client device2002 may obtain time information from server 2001 and generateobtainment time information using the obtained time information. Clientdevice 2002 may also obtain location information from server 2001 andgenerate obtainment location information using the obtained locationinformation.

For example, regarding time information, server 2001 and client device2002 perform clock synchronization using a means such as the NetworkTime Protocol (NTP) or the Precision Time Protocol (PTP). This enablesclient device 2002 to obtain accurate time information. What's more,since it is possible to synchronize clocks between server 2001 andclient devices 2002, it is possible to synchronize times included inpieces of sensor information obtained by separate client devices 2002.As a result, server 2001 can handle sensor information indicating asynchronized time. It should be noted that a means of synchronizingclocks may be any means other than the NTP or PTP. In addition, GPSinformation may be used as the time information and the locationinformation.

Server 2001 may specify a time or a location and obtain pieces of sensorinformation from client devices 2002. For example, when an accidentoccurs, in order to search for a client device in the vicinity of theaccident, server 2001 specifies an accident occurrence time and anaccident occurrence location and broadcasts sensor informationtransmission requests to client devices 2002. Then, client device 2002having sensor information obtained at the corresponding time andlocation transmits the sensor information to server 2001. In otherwords, client device 2002 receives, from server 2001, a sensorinformation transmission request including specification informationspecifying a location and a time. When sensor information obtained at alocation and a time indicated by the specification information is storedin storage 2012, and client device 2002 determines that the mobileobject is present in the environment in which the mobile object cantransmit the sensor information to server 2001, client device 2002transmits, to server 2001, the sensor information obtained at thelocation and the time indicated by the specification information.Consequently, server 2001 can obtain the pieces of sensor informationpertaining to the occurrence of the accident from client devices 2002,and use the pieces of sensor information for accident analysis etc.

It should be noted that when client device 2002 receives a sensorinformation transmission request from server 2001, client device 2002may refuse to transmit sensor information. Additionally, client device2002 may set in advance which pieces of sensor information can betransmitted. Alternatively, server 2001 may inquire of client device2002 each time whether sensor information can be transmitted.

A point may be given to client device 2002 that has transmitted sensorinformation to server 2001. This point can be used in payment for, forexample, gasoline expenses, electric vehicle (EV) charging expenses, ahighway toll, or rental car expenses. After obtaining sensorinformation, server 2001 may delete information for specifying clientdevice 2002 that has transmitted the sensor information. For example,this information is a network address of client device 2002. Since thisenables the anonymization of sensor information, a user of client device2002 can securely transmit sensor information from client device 2002 toserver 2001. Server 2001 may include servers. For example, by serverssharing sensor information, even when one of the servers breaks down,the other servers can communicate with client device 2002. Accordingly,it is possible to avoid service outage due to a server breakdown.

A specified location specified by a sensor information transmissionrequest indicates an accident occurrence location etc., and may bedifferent from a position of client device 2002 at a specified timespecified by the sensor information transmission request. For thisreason, for example, by specifying, as a specified location, a rangesuch as within XX meters of a surrounding area, server 2001 can requestinformation from client device 2002 within the range. Similarly, server2001 may also specify, as a specified time, a range such as within Nseconds before and after a certain time. As a result, server 2001 canobtain sensor information from client device 2002 present for a timefrom t-N to t+N and in a location within XX meters from absoluteposition S. When client device 2002 transmits three-dimensional datasuch as LiDAR, client device 2002 may transmit data created immediatelyafter time t.

Server 2001 may separately specify information indicating, as aspecified location, a location of client device 2002 from which sensorinformation is to be obtained, and a location at which sensorinformation is desirably obtained. For example, server 2001 specifiesthat sensor information including at least a range within YY meters fromabsolute position S is to be obtained from client device 2002 presentwithin XX meters from absolute position S. When client device 2002selects three-dimensional data to be transmitted, client device 2002selects one or more pieces of three-dimensional data so that the one ormore pieces of three-dimensional data include at least the sensorinformation including the specified range. Each of the one or morepieces of three-dimensional data is a random-accessible unit of data. Inaddition, when client device 2002 transmits a visible light image,client device 2002 may transmit pieces of temporally continuous imagedata including at least a frame immediately before or immediately aftertime t.

When client device 2002 can use physical networks such as 5G, Wi-Fi, ormodes in 5G for transmitting sensor information, client device 2002 mayselect a network to be used according to the order of priority notifiedby server 2001. Alternatively, client device 2002 may select a networkthat enables client device 2002 to ensure an appropriate bandwidth basedon the size of transmit data. Alternatively, client device 2002 mayselect a network to be used, based on data transmission expenses etc. Atransmission request from server 2001 may include information indicatinga transmission deadline, for example, performing transmission whenclient device 2002 can start transmission by time t. When server 2001cannot obtain sufficient sensor information within a time limit, server2001 may issue a transmission request again.

Sensor information may include header information indicatingcharacteristics of sensor data along with compressed or uncompressedsensor data. Client device 2002 may transmit header information toserver 2001 via a physical network or a communication protocol that isdifferent from a physical network or a communication protocol used forsensor data. For example, client device 2002 transmits headerinformation to server 2001 prior to transmitting sensor data. Server2001 determines whether to obtain the sensor data of client device 2002,based on a result of analysis of the header information. For example,header information may include information indicating a point cloudobtainment density, an elevation angle, or a frame rate of LiDAR, orinformation indicating, for example, a resolution, an SN ratio, or aframe rate of a visible light image. Accordingly, server 2001 can obtainthe sensor information from client device 2002 having the sensor data ofdetermined quality.

As stated above, client device 2002 is provided in the mobile object,obtains sensor information that has been obtained by a sensor providedin the mobile object and indicates a surrounding state of the mobileobject, and stores the sensor information into storage 2012. Clientdevice 2002 determines whether the mobile object is present in anenvironment in which the mobile object is capable of transmitting thesensor information to server 2001, and transmits the sensor informationto server 2001 when the mobile object is determined to be present in theenvironment in which the mobile object is capable of transmitting thesensor information to server 2001.

Additionally, client device 2002 further creates, from the sensorinformation, three-dimensional data of a surrounding area of the mobileobject, and estimates a self-location of the mobile object using thethree-dimensional data created.

Besides, client device 2002 further transmits a transmission request fora three-dimensional map to server 2001, and receives thethree-dimensional map from server 2001. In the estimating, client device2002 estimates the self-location using the three-dimensional data andthe three-dimensional map.

It should be noted that the above process performed by client device2002 may be realized as an information transmission method for use inclient device 2002.

In addition, client device 2002 may include a processor and memory.Using the memory, the processor may perform the above process.

Next, a sensor information collection system according to the presentembodiment will be described. FIG. 21 is a diagram illustrating aconfiguration of the sensor information collection system according tothe present embodiment. As illustrated in FIG. 21, the sensorinformation collection system according to the present embodimentincludes terminal 2021A, terminal 2021B, communication device 2022A,communication device 2022B, network 2023, data collection server 2024,map server 2025, and client device 2026. It should be noted that whenterminal 2021A and terminal 2021B are not particularly distinguished,terminal 2021A and terminal 2021B are also referred to as terminal 2021.Additionally, when communication device 2022A and communication device2022B are not particularly distinguished, communication device 2022A andcommunication device 2022B are also referred to as communication device2022.

Data collection server 2024 collects data such as sensor data obtainedby a sensor included in terminal 2021 as position-related data in whichthe data is associated with a position in a three-dimensional space.

Sensor data is data obtained by, for example, detecting a surroundingstate of terminal 2021 or an internal state of terminal 2021 using asensor included in terminal 2021. Terminal 2021 transmits, to datacollection server 2024, one or more pieces of sensor data collected fromone or more sensor devices in locations at which direct communicationwith terminal 2021 is possible or at which communication with terminal2021 is possible by the same communication system or via one or morerelay devices.

Data included in position-related data may include, for example,information indicating an operating state, an operating log, a serviceuse state, etc. of a terminal or a device included in the terminal. Inaddition, the data include in the position-related data may include, forexample, information in which an identifier of terminal 2021 isassociated with a position or a movement path etc. of terminal 2021.

Information indicating a position included in position-related data isassociated with, for example, information indicating a position inthree-dimensional data such as three-dimensional map data. The detailsof information indicating a position will be described later.

Position-related data may include at least one of the above-describedtime information or information indicating an attribute of data includedin the position-related data or a type (e.g., a model number) of asensor that has created the data, in addition to position informationthat is information indicating a position. The position information andthe time information may be stored in a header area of theposition-related data or a header area of a frame that stores theposition-related data. Further, the position information and the timeinformation may be transmitted and/or stored as metadata associated withthe position-related data, separately from the position-related data.

Map server 2025 is connected to, for example, network 2023, andtransmits three-dimensional data such as three-dimensional map data inresponse to a request from another device such as terminal 2021.Besides, as described in the aforementioned embodiments, map server 2025may have, for example, a function of updating three-dimensional datausing sensor information transmitted from terminal 2021.

Data collection server 2024 is connected to, for example, network 2023,collects position-related data from another device such as terminal2021, and stores the collected position-related data into a storage ofdata collection server 2024 or a storage of another server. In addition,data collection server 2024 transmits, for example, metadata ofcollected position-related data or three-dimensional data generatedbased on the position-related data, to terminal 2021 in response to arequest from terminal 2021.

Network 2023 is, for example, a communication network such as theInternet. Terminal 2021 is connected to network 2023 via communicationdevice 2022. Communication device 2022 communicates with terminal 2021using one communication system or switching between communicationsystems. Communication device 2022 is a communication satellite thatperforms communication using, for example, (1) a base station compliantwith Long-Term Evolution (LTE) etc., (2) an access point (AP) for Wi-Fior millimeter-wave communication etc., (3) a low-power wide-area (LPWA)network gateway such as SIGFOX, LoRaWAN, or Wi-SUN, or (4) a satellitecommunication system such as DVB-S2.

It should be noted that a base station may communicate with terminal2021 using a system classified as an LPWA network such as NarrowbandInternet of Things (NB IoT) or LTE-M, or switching between thesesystems.

Here, although, in the example given, terminal 2021 has a function ofcommunicating with communication device 2022 that uses two types ofcommunication systems, and communicates with map server 2025 or datacollection server 2024 using one of the communication systems orswitching between the communication systems and between communicationdevices 2022 to be a direct communication partner; a configuration ofthe sensor information collection system and terminal 2021 is notlimited to this. For example, terminal 2021 need not have a function ofperforming communication using communication systems, and may have afunction of performing communication using one of the communicationsystems. Terminal 2021 may also support three or more communicationsystems. Additionally, each terminal 2021 may support a differentcommunication system.

Terminal 2021 includes, for example, the configuration of client device902 illustrated in FIG. 11. Terminal 2021 estimates a self-location etc.using received three-dimensional data. Besides, terminal 2021 associatessensor data obtained from a sensor and position information obtained byself-location estimation to generate position-related data.

Position information appended to position-related data indicates, forexample, a position in a coordinate system used for three-dimensionaldata. For example, the position information is coordinate valuesrepresented using a value of a latitude and a value of a longitude.Here, terminal 2021 may include, in the position information, acoordinate system serving as a reference for the coordinate values andinformation indicating three-dimensional data used for locationestimation, along with the coordinate values. Coordinate values may alsoinclude altitude information.

The position information may be associated with a data unit or a spaceunit usable for encoding the above three-dimensional data. Such a unitis, for example, WLD, GOS, SPC, VLM, or VXL. Here, the positioninformation is represented by, for example, an identifier foridentifying a data unit such as the SPC corresponding toposition-related data. It should be noted that the position informationmay include, for example, information indicating three-dimensional dataobtained by encoding a three-dimensional space including a data unitsuch as the SPC or information indicating a detailed position within theSPC, in addition to the identifier for identifying the data unit such asthe SPC. The information indicating the three-dimensional data is, forexample, a file name of the three-dimensional data.

As stated above, by generating position-related data associated withposition information based on location estimation usingthree-dimensional data, the system can give more accurate positioninformation to sensor information than when the system appends positioninformation based on a self-location of a client device (terminal 2021)obtained using a GPS to sensor information. As a result, even whenanother device uses the position-related data in another service, thereis a possibility of more accurately determining a position correspondingto the position-related data in an actual space, by performing locationestimation based on the same three-dimensional data.

It should be noted that although the data transmitted from terminal 2021is the position-related data in the example given in the presentembodiment, the data transmitted from terminal 2021 may be dataunassociated with position information. In other words, the transmissionand reception of three-dimensional data or sensor data described in theother embodiments may be performed via network 2023 described in thepresent embodiment.

Next, a different example of position information indicating a positionin a three-dimensional or two-dimensional actual space or in a map spacewill be described. The position information appended to position-relateddata may be information indicating a relative position relative to akeypoint in three-dimensional data. Here, the keypoint serving as areference for the position information is encoded as, for example, SWLD,and notified to terminal 2021 as three-dimensional data.

The information indicating the relative position relative to thekeypoint may be, for example, information that is represented by avector from the keypoint to the point indicated by the positioninformation, and indicates a direction and a distance from the keypointto the point indicated by the position information. Alternatively, theinformation indicating the relative position relative to the keypointmay be information indicating an amount of displacement from thekeypoint to the point indicated by the position information along eachof the x axis, the y axis, and the z axis. Additionally, the informationindicating the relative position relative to the keypoint may beinformation indicating a distance from each of three or more keypointsto the point indicated by the position information. It should be notedthat the relative position need not be a relative position of the pointindicated by the position information represented using each keypoint asa reference, and may be a relative position of each keypoint representedwith respect to the point indicated by the position information.Examples of position information based on a relative position relativeto a keypoint include information for identifying a keypoint to be areference, and information indicating the relative position of the pointindicated by the position information and relative to the keypoint. Whenthe information indicating the relative position relative to thekeypoint is provided separately from three-dimensional data, theinformation indicating the relative position relative to the keypointmay include, for example, coordinate axes used in deriving the relativeposition, information indicating a type of the three-dimensional data,and/or information indicating a magnitude per unit amount (e.g., ascale) of a value of the information indicating the relative position.

The position information may include, for each keypoint, informationindicating a relative position relative to the keypoint. When theposition information is represented by relative positions relative tokeypoints, terminal 2021 that intends to identify a position in anactual space indicated by the position information may calculatecandidate points of the position indicated by the position informationfrom positions of the keypoints each estimated from sensor data, and maydetermine that a point obtained by averaging the calculated candidatepoints is the point indicated by the position information. Since thisconfiguration reduces the effects of errors when the positions of thekeypoints are estimated from the sensor data, it is possible to improvethe estimation accuracy for the point in the actual space indicated bythe position information. Besides, when the position informationincludes information indicating relative positions relative tokeypoints, if it is possible to detect any one of the keypointsregardless of the presence of keypoints undetectable due to a limitationsuch as a type or performance of a sensor included in terminal 2021, itis possible to estimate a value of the point indicated by the positioninformation.

A point identifiable from sensor data can be used as a keypoint.Examples of the point identifiable from the sensor data include a pointor a point within a region that satisfies a predetermined keypointdetection condition, such as the above-described three-dimensionalfeature or feature of visible light data is greater than or equal to athreshold value.

Moreover, a marker etc. placed in an actual space may be used as akeypoint. In this case, the maker may be detected and located from dataobtained using a sensor such as LiDER or a camera. For example, themarker may be represented by a change in color or luminance value(degree of reflection), or a three-dimensional shape (e.g., unevenness).Coordinate values indicating a position of the marker, or atwo-dimensional bar code or a bar code etc. generated from an identifierof the marker may be also used.

Furthermore, a light source that transmits an optical signal may be usedas a marker. When a light source of an optical signal is used as amarker, not only information for obtaining a position such as coordinatevalues or an identifier but also other data may be transmitted using anoptical signal. For example, an optical signal may include contents ofservice corresponding to the position of the marker, an address forobtaining contents such as a URL, or an identifier of a wirelesscommunication device for receiving service, and information indicating awireless communication system etc. for connecting to the wirelesscommunication device. The use of an optical communication device (alight source) as a marker not only facilitates the transmission of dataother than information indicating a position but also makes it possibleto dynamically change the data.

Terminal 2021 finds out a correspondence relationship of keypointsbetween mutually different data using, for example, a common identifierused for the data, or information or a table indicating thecorrespondence relationship of the keypoints between the data. Whenthere is no information indicating a correspondence relationship betweenkeytpoints, terminal 2021 may also determine that when coordinates of akeypoint in three-dimensional data are converted into a position in aspace of another three-dimensional data, a keypoint closest to theposition is a corresponding keypoint.

When the position information based on the relative position describedabove is used, terminal 2021 that uses mutually differentthree-dimensional data or services can identify or estimate a positionindicated by the position information with respect to a common keypointincluded in or associated with each three-dimensional data. As a result,terminal 2021 that uses the mutually different three-dimensional data orthe services can identify or estimate the same position with higheraccuracy.

Even when map data or three-dimensional data represented using mutuallydifferent coordinate systems are used, since it is possible to reducethe effects of errors caused by the conversion of a coordinate system,it is possible to coordinate services based on more accurate positioninformation.

Hereinafter, an example of functions provided by data collection server2024 will be described. Data collection server 2024 may transferreceived position-related data to another data server. When there aredata servers, data collection server 2024 determines to which dataserver received position-related data is to be transferred, andtransfers the position-related data to a data server determined as atransfer destination.

Data collection server 2024 determines a transfer destination based on,for example, transfer destination server determination rules preset todata collection server 2024. The transfer destination serverdetermination rules are set by, for example, a transfer destinationtable in which identifiers respectively associated with terminals 2021are associated with transfer destination data servers.

Terminal 2021 appends an identifier associated with terminal 2021 toposition-related data to be transmitted, and transmits theposition-related data to data collection server 2024. Data collectionserver 2024 determines a transfer destination data server correspondingto the identifier appended to the position-related data, based on thetransfer destination server determination rules set out using thetransfer destination table etc.; and transmits the position-related datato the determined data server. The transfer destination serverdetermination rules may be specified based on a determination conditionset using a time, a place, etc. at which position-related data isobtained. Here, examples of the identifier associated with transmissionsource terminal 2021 include an identifier unique to each terminal 2021or an identifier indicating a group to which terminal 2021 belongs.

The transfer destination table need not be a table in which identifiersassociated with transmission source terminals are directly associatedwith transfer destination data servers. For example, data collectionserver 2024 holds a management table that stores tag informationassigned to each identifier unique to terminal 2021, and a transferdestination table in which the pieces of tag information are associatedwith transfer destination data servers. Data collection server 2024 maydetermine a transfer destination data server based on tag information,using the management table and the transfer destination table. Here, thetag information is, for example, control information for management orcontrol information for providing service assigned to a type, a modelnumber, an owner of terminal 2021 corresponding to the identifier, agroup to which terminal 2021 belongs, or another identifier. Moreover,in the transfer destination able, identifiers unique to respectivesensors may be used instead of the identifiers associated withtransmission source terminals 2021. Furthermore, the transferdestination server determination rules may be set by client device 2026.

Data collection server 2024 may determine data servers as transferdestinations, and transfer received position-related data to the dataservers. According to this configuration, for example, whenposition-related data is automatically backed up or when, in order thatposition-related data is commonly used by different services, there is aneed to transmit the position-related data to a data server forproviding each service, it is possible to achieve data transfer asintended by changing a setting of data collection server 2024. As aresult, it is possible to reduce the number of steps necessary forbuilding and changing a system, compared to when a transmissiondestination of position-related data is set for each terminal 2021.

Data collection server 2024 may register, as a new transfer destination,a data server specified by a transfer request signal received from adata server; and transmit position-related data subsequently received tothe data server, in response to the transfer request signal.

Data collection server 2024 may store position-related data receivedfrom terminal 2021 into a recording device, and transmitposition-related data specified by a transmission request signalreceived from terminal 2021 or a data server to request source terminal2021 or the data server in response to the transmission request signal.

Data collection server 2024 may determine whether position-related datais suppliable to a request source data server or terminal 2021, andtransfer or transmit the position-related data to the request sourcedata server or terminal 2021 when determining that the position-relateddata is suppliable.

When data collection server 2024 receives a request for currentposition-related data from client device 2026, even if it is not atiming for transmitting position-related data by terminal 2021, datacollection server 2024 may send a transmission request for the currentposition-related data to terminal 2021, and terminal 2021 may transmitthe current position-related data in response to the transmissionrequest.

Although terminal 2021 transmits position information data to datacollection server 2024 in the above description, data collection server2024 may have a function of managing terminal 2021 such as a functionnecessary for collecting position-related data from terminal 2021 or afunction used when collecting position-related data from terminal 2021.

Data collection server 2024 may have a function of transmitting, toterminal 2021, a data request signal for requesting transmission ofposition information data, and collecting position-related data.

Management information such as an address for communicating withterminal 2021 from which data is to be collected or an identifier uniqueto terminal 2021 is registered in advance in data collection server2024. Data collection server 2024 collects position-related data fromterminal 2021 based on the registered management information. Managementinformation may include information such as types of sensors included interminal 2021, the number of sensors included in terminal 2021, andcommunication systems supported by terminal 2021.

Data collection server 2024 may collect information such as an operatingstate or a current position of terminal 2021 from terminal 2021.

Registration of management information may be instructed by clientdevice 2026, or a process for the registration may be started byterminal 2021 transmitting a registration request to data collectionserver 2024. Data collection server 2024 may have a function ofcontrolling communication between data collection server 2024 andterminal 2021.

Communication between data collection server 2024 and terminal 2021 maybe established using a dedicated line provided by a service providersuch as a mobile network operator (MNO) or a mobile virtual networkoperator (MVNO), or a virtual dedicated line based on a virtual privatenetwork (VPN). According to this configuration, it is possible toperform secure communication between terminal 2021 and data collectionserver 2024.

Data collection server 2024 may have a function of authenticatingterminal 2021 or a function of encrypting data to be transmitted andreceived between data collection server 2024 and terminal 2021. Here,the authentication of terminal 2021 or the encryption of data isperformed using, for example, an identifier unique to terminal 2021 oran identifier unique to a terminal group including terminals 2021, whichis shared in advance between data collection server 2024 and terminal2021. Examples of the identifier include an international mobilesubscriber identity (IMSI) that is a unique number stored in asubscriber identity module (SIM) card. An identifier for use inauthentication and an identifier for use in encryption of data may beidentical or different.

The authentication or the encryption of data between data collectionserver 2024 and terminal 2021 is feasible when both data collectionserver 2024 and terminal 2021 have a function of performing the process.The process does not depend on a communication system used bycommunication device 2022 that performs relay. Accordingly, since it ispossible to perform the common authentication or encryption withoutconsidering whether terminal 2021 uses a communication system, theuser's convenience of system architecture is increased. However, theexpression “does not depend on a communication system used bycommunication device 2022 that performs relay” means a change accordingto a communication system is not essential. In other words, in order toimprove the transfer efficiency or ensure security, the authenticationor the encryption of data between data collection server 2024 andterminal 2021 may be changed according to a communication system used bya relay device.

Data collection server 2024 may provide client device 2026 with a UserInterface (UI) that manages data collection rules such as types ofposition-related data collected from terminal 2021 and data collectionschedules. Accordingly, a user can specify, for example, terminal 2021from which data is to be collected using client device 2026, a datacollection time, and a data collection frequency. Additionally, datacollection server 2024 may specify, for example, a region on a map fromwhich data is to be desirably collected, and collect position-relateddata from terminal 2021 included in the region.

When the data collection rules are managed on a per terminal 2021 basis,client device 2026 presents, on a screen, a list of terminals 2021 orsensors to be managed. The user sets, for example, a necessity for datacollection or a collection schedule for each item in the list.

When a region on a map from which data is to be desirably collected isspecified, client device 2026 presents, on a screen, a two-dimensionalor three-dimensional map of a region to be managed. The user selects theregion from which data is to be collected on the displayed map. Examplesof the region selected on the map include a circular or rectangularregion having a point specified on the map as the center, or a circularor rectangular region specifiable by a drag operation. Client device2026 may also select a region in a preset unit such as a city, an areaor a block in a city, or a main road, etc. Instead of specifying aregion using a map, a region may be set by inputting values of alatitude and a longitude, or a region may be selected from a list ofcandidate regions derived based on inputted text information. Textinformation is, for example, a name of a region, a city, or a landmark.

Moreover, data may be collected while the user dynamically changes aspecified region by specifying one or more terminals 2021 and setting acondition such as within 100 meters of one or more terminals 2021.

When client device 2026 includes a sensor such as a camera, a region ona map may be specified based on a position of client device 2026 in anactual space obtained from sensor data. For example, client device 2026may estimate a self-location using sensor data, and specify, as a regionfrom which data is to be collected, a region within a predetermineddistance from a point on a map corresponding to the estimated locationor a region within a distance specified by the user. Client device 2026may also specify, as the region from which the data is to be collected,a sensing region of the sensor, that is, a region corresponding toobtained sensor data. Alternatively, client device 2026 may specify, asthe region from which the data is to be collected, a region based on alocation corresponding to sensor data specified by the user. Eitherclient device 2026 or data collection server 2024 may estimate a regionon a map or a location corresponding to sensor data.

When a region on a map is specified, data collection server 2024 mayspecify terminal 2021 within the specified region by collecting currentposition information of each terminal 2021, and may send a transmissionrequest for position-related data to specified terminal 2021. When datacollection server 2024 transmits information indicating a specifiedregion to terminal 2021, determines whether terminal 2021 is presentwithin the specified region, and determines that terminal 2021 ispresent within the specified region, rather than specifying terminal2021 within the region, terminal 2021 may transmit position-relateddata.

Data collection server 2024 transmits, to client device 2026, data suchas a list or a map for providing the above-described User Interface (UI)in an application executed by client device 2026. Data collection server2024 may transmit, to client device 2026, not only the data such as thelist or the map but also an application program. Additionally, the aboveUI may be provided as contents created using HTML displayable by abrowser. It should be noted that part of data such as map data may besupplied from a server, such as map server 2025, other than datacollection server 2024.

When client device 2026 receives an input for notifying the completionof an input such as pressing of a setup key by the user, client device2026 transmits the inputted information as configuration information todata collection server 2024. Data collection server 2024 transmits, toeach terminal 2021, a signal for requesting position-related data ornotifying position-related data collection rules, based on theconfiguration information received from client device 2026, and collectsthe position-related data.

Next, an example of controlling operation of terminal 2021 based onadditional information added to three-dimensional or two-dimensional mapdata will be described.

In the present configuration, object information that indicates aposition of a power feeding part such as a feeder antenna or a feedercoil for wireless power feeding buried under a road or a parking lot isincluded in or associated with three-dimensional data, and such objectinformation is provided to terminal 2021 that is a vehicle or a drone.

A vehicle or a drone that has obtained the object information to getcharged automatically moves so that a position of a charging part suchas a charging antenna or a charging coil included in the vehicle or thedrone becomes opposite to a region indicated by the object information,and such vehicle or a drone starts to charge itself. It should be notedthat when a vehicle or a drone has no automatic driving function, adirection to move in or an operation to perform is presented to a driveror an operator by using an image displayed on a screen, audio, etc. Whena position of a charging part calculated based on an estimatedself-location is determined to fall within the region indicated by theobject information or a predetermined distance from the region, an imageor audio to be presented is changed to a content that puts a stop todriving or operating, and the charging is started.

Object information need not be information indicating a position of apower feeding part, and may be information indicating a region withinwhich placement of a charging part results in a charging efficiencygreater than or equal to a predetermined threshold value. A positionindicated by object information may be represented by, for example, thecentral point of a region indicated by the object information, a regionor a line within a two-dimensional plane, or a region, a line, or aplane within a three-dimensional space.

According to this configuration, since it is possible to identify theposition of the power feeding antenna unidentifiable by sensing data ofLiDER or an image captured by the camera, it is possible to highlyaccurately align a wireless charging antenna included in terminal 2021such as a vehicle with a wireless power feeding antenna buried under aroad. As a result, it is possible to increase a charging speed at thetime of wireless charging and improve the charging efficiency.

Object information may be an object other than a power feeding antenna.For example, three-dimensional data includes, for example, a position ofan AP for millimeter-wave wireless communication as object information.Accordingly, since terminal 2021 can identify the position of the AP inadvance, terminal 2021 can steer a directivity of beam to a direction ofthe object information and start communication. As a result, it ispossible to improve communication quality such as increasingtransmission rates, reducing the duration of time before startingcommunication, and extending a communicable period.

Object information may include information indicating a type of anobject corresponding to the object information. In addition, whenterminal 2021 is present within a region in an actual spacecorresponding to a position in three-dimensional data of the objectinformation or within a predetermined distance from the region, theobject information may include information indicating a process to beperformed by terminal 2021.

Object information may be provided by a server different from a serverthat provides three-dimensional data. When object information isprovided separately from three-dimensional data, object groups in whichobject information used by the same service is stored may be eachprovided as separate data according to a type of a target service or atarget device.

Three-dimensional data used in combination with object information maybe point cloud data of WLD or keypoint data of SWLD.

Embodiment 6

Hereinafter, a scan order of an octree representation and voxels will bedescribed. A volume is encoded after being converted into an octreestructure (made into an octree). The octree structure includes nodes andleaves. Each node has eight nodes or leaves, and each leaf has voxel(VXL) information. FIG. 22 is a diagram showing an example structure ofa volume including voxels. FIG. 23 is a diagram showing an example ofthe volume shown in FIG. 22 having been converted into the octreestructure. Among the leaves shown in FIG. 23, leaves 1, 2, and 3respectively represent VXL 1, VXL 2, and VXL 3, and represent VXLsincluding a point group (hereinafter, active VXLs).

An octree is represented by, for example, binary sequences of 1s and 0s.For example, when giving the nodes or the active VXLs a value of 1 andeverything else a value of 0, each node and leaf is assigned with thebinary sequence shown in FIG. 23. Thus, this binary sequence is scannedin accordance with a breadth-first or a depth-first scan order. Forexample, when scanning breadth-first, the binary sequence shown in A ofFIG. 24 is obtained. When scanning depth-first, the binary sequenceshown in B of FIG. 24 is obtained. The binary sequences obtained throughthis scanning are encoded through entropy encoding, which reduces anamount of information.

Depth information in the octree representation will be described next.Depth in the octree representation is used in order to control up to howfine a granularity point cloud information included in a volume isstored. Upon setting a great depth, it is possible to reproduce thepoint cloud information to a more precise level, but an amount of datafor representing the nodes and leaves increases. Upon setting a smalldepth, however, the amount of data decreases, but some information thatthe point cloud information originally held is lost, since pieces ofpoint cloud information including different positions and differentcolors are now considered as pieces of point cloud information includingthe same position and the same color.

For example, FIG. 25 is a diagram showing an example in which the octreewith a depth of 2 shown in FIG. 23 is represented with a depth of 1. Theoctree shown in FIG. 25 has a lower amount of data than the octree shownin FIG. 23. In other words, the binarized octree shown in FIG. 25 has alower bit count than the octree shown in FIG. 23. Leaf 1 and leaf 2shown in FIG. 23 are represented by leaf 1 shown in FIG. 24. In otherwords, the information on leaf 1 and leaf 2 being in different positionsis lost.

FIG. 26 is a diagram showing a volume corresponding to the octree shownin FIG. 25. VXL 1 and VXL 2 shown in FIG. 22 correspond to VXL 12 shownin FIG. 26. In this case, the three-dimensional data encoding devicegenerates color information of VXL 12 shown in FIG. 26 using colorinformation of VXL 1 and VXL 2 shown in FIG. 22. For example, thethree-dimensional data encoding device calculates an average value, amedian, a weighted average value, or the like of the color informationof VXL 1 and VXL 2 as the color information of VXL 12. In this manner,the three-dimensional data encoding device may control a reduction ofthe amount of data by changing the depth of the octree.

The three-dimensional data encoding device may set the depth informationof the octree to units of worlds, units of spaces, or units of volumes.In this case, the three-dimensional data encoding device may append thedepth information to header information of the world, header informationof the space, or header information of the volume. In all worlds,spaces, and volumes associated with different times, the same value maybe used as the depth information. In this case, the three-dimensionaldata encoding device may append the depth information to headerinformation managing the worlds associated with all times.

Embodiment 7

Information of a three-dimensional point cloud includes geometryinformation (geometry) and attribute information (attribute). Geometryinformation includes coordinates (x-coordinate, y-coordinate,z-coordinate) with respect to a certain point. When geometry informationis encoded, a method of representing the position of each ofthree-dimensional points in octree representation and encoding theoctree information to reduce a code amount is used instead of directlyencoding the coordinates of the three-dimensional point.

On the other hand, attribute information includes informationindicating, for example, color information (RGB, YUV, etc.) of eachthree-dimensional point, a reflectance, and a normal vector. Forexample, a three-dimensional data encoding device is capable of encodingattribute information using an encoding method different from a methodused to encode geometry information.

In the present embodiment, a method of encoding attribute information isexplained. It is to be noted that, in the present embodiment, the methodis explained based on an example case using integer values as values ofattribute information. For example, when each of RGB or YUV colorcomponents is of an 8-bit accuracy, the color component is an integervalue in a range from 0 to 255. When a reflectance value is of 10-bitaccuracy, the reflectance value is an integer in a range from 0 to 1023.It is to be noted that, when the bit accuracy of attribute informationis a decimal accuracy, the three-dimensional data encoding device maymultiply the value by a scale value to round it to an integer value sothat the value of the attribute information becomes an integer value. Itis to be noted that the three-dimensional data encoding device may addthe scale value to, for example, a header of a bitstream.

As a method of encoding attribute information of a three-dimensionalpoint, it is conceivable to calculate a predicted value of the attributeinformation of the three-dimensional point and encode a difference(prediction residual) between the original value of the attributeinformation and the predicted value. For example, when the value ofattribute information at three-dimensional point p is Ap and a predictedvalue is Pp, the three-dimensional data encoding device encodesdifferential absolute value Diffp=|Ap−Pp|. In this case, whenhighly-accurate predicted value Pp can be generated, differentialabsolute value Diffp is small. Thus, for example, it is possible toreduce the code amount by entropy encoding differential absolute valueDiffp using a coding table that reduces an occurrence bit count morewhen differential absolute value Diffp is smaller.

As a method of generating a prediction value of attribute information,it is conceivable to use attribute information of a referencethree-dimensional point that is another three-dimensional point whichneighbors a current three-dimensional point to be encoded. Here, areference three-dimensional point is a three-dimensional point in arange of a predetermined distance from the current three-dimensionalpoint. For example, when there are current three-dimensional pointp=(x1, y1, z1) and three-dimensional point q=(x2, y2, z2), thethree-dimensional data encoding device calculates Euclidean distance d(p, q) between three-dimensional point p and three-dimensional point qrepresented by (Equation A1).

[Math. 1]

d(p,q)=√{square root over ((x1−y1)²+(x2−y2)²+(x3−y3)²)}  (Equation A1)

The three-dimensional data encoding device determines that the positionof three-dimensional point q is closer to the position of currentthree-dimensional point p when Euclidean distance d (p, q) is smallerthan predetermined threshold value THd, and determines to use the valueof the attribute information of three-dimensional point q to generate apredicted value of the attribute information of currentthree-dimensional point p. It is to be noted that the method ofcalculating the distance may be another method, and a Mahalanobisdistance or the like may be used. In addition, the three-dimensionaldata encoding device may determine not to use, in prediction processing,any three-dimensional point outside the predetermined range of distancefrom the current three-dimensional point. For example, whenthree-dimensional point r is present, and distance d (p, r) betweencurrent three-dimensional point p and three-dimensional point r islarger than or equal to threshold value THd, the three-dimensional dataencoding device may determine not to use three-dimensional point r forprediction. It is to be noted that the three-dimensional data encodingdevice may add the information indicating threshold value THd to, forexample, a header of a bitstream.

FIG. 27 is a diagram illustrating an example of three-dimensionalpoints. In this example, distance d (p, q) between currentthree-dimensional point p and three-dimensional point q is smaller thanthreshold value THd. Thus, the three-dimensional data encoding devicedetermines that three-dimensional point q is a referencethree-dimensional point of current three-dimensional point p, anddetermines to use the value of attribute information Aq ofthree-dimensional point q to generate predicted value Pp of attributeinformation Ap of current three-dimensional point p.

In contrast, distance d (p, r) between current three-dimensional point pand three-dimensional point r is larger than or equal to threshold valueTHd. Thus, the three-dimensional data encoding device determines thatthree-dimensional point r is not any reference three-dimensional pointof current three-dimensional point p, and determines not to use thevalue of attribute information Ar of three-dimensional point r togenerate predicted value Pp of attribute information Ap of currentthree-dimensional point p.

In addition, when encoding the attribute information of the currentthree-dimensional point using a predicted value, the three-dimensionaldata encoding device uses a three-dimensional point whose attributeinformation has already been encoded and decoded, as a referencethree-dimensional point. Likewise, when decoding the attributeinformation of a current three-dimensional point to be decoded, thethree-dimensional data decoding device uses a three-dimensional pointwhose attribute information has already been decoded, as a referencethree-dimensional point. In this way, it is possible to generate thesame predicted value at the time of encoding and decoding. Thus, abitstream of the three-dimensional point generated by the encoding canbe decoded correctly at the decoding side.

Furthermore, when encoding attribute information of each ofthree-dimensional points, it is conceivable to classify thethree-dimensional point into one of a plurality of layers using geometryinformation of the three-dimensional point and then encode the attributeinformation. Here, each of the layers classified is referred to as aLevel of Detail (LoD). A method of generating LoDs is explained withreference to FIG. 28.

First, the three-dimensional data encoding device selects initial pointa0 and assigns initial point a0 to LoD0. Next, the three-dimensionaldata encoding device extracts point a1 distant from point a0 more thanthreshold value Thres_LoD[0] of LoD0 and assigns point a1 to LoD0. Next,the three-dimensional data encoding device extracts point a2 distantfrom point a1 more than threshold value Thres_LoD[0] of LoD0 and assignspoint a2 to LoD0. In this way, the three-dimensional data encodingdevice configures LoD0 in such a manner that the distance between thepoints in LoD0 is larger than threshold value Thres_LoD[0].

Next, the three-dimensional data encoding device selects point b0 whichhas not yet been assigned to any LoD and assigns point b0 to LoD1. Next,the three-dimensional data encoding device extracts point b1 which isdistant from point b0 more than threshold value Thres_LoD[1] of LoD1 andwhich has not yet been assigned to any LoD, and assigns point b1 toLoD1. Next, the three-dimensional data encoding device extracts point b2which is distant from point b1 more than threshold value Thres_LoD[1] ofLoD1 and which has not yet been assigned to any LoD, and assigns pointb2 to LoD1. In this way, the three-dimensional data encoding deviceconfigures LoD1 in such a manner that the distance between the points inLoD1 is larger than threshold value Thres_LoD[1].

Next, the three-dimensional data encoding device selects point c0 whichhas not yet been assigned to any LoD and assigns point c0 to LoD2. Next,the three-dimensional data encoding device extracts point c1 which isdistant from point c0 more than threshold value Thres_LoD[2] of LoD2 andwhich has not yet been assigned to any LoD, and assigns point c1 toLoD2. Next, the three-dimensional data encoding device extracts point c2which is distant from point c1 more than threshold value Thres_LoD[2] ofLoD2 and which has not yet been assigned to any LoD, and assigns pointc2 to LoD2. In this way, the three-dimensional data encoding deviceconfigures LoD2 in such a manner that the distance between the points inLoD2 is larger than threshold value Thres_LoD[2]. For example, asillustrated in FIG. 29, threshold values Thres_LoD[0], Thres_LoD[1], andThres_LoD[2] of respective LoDs are set.

In addition, the three-dimensional data encoding device may add theinformation indicating the threshold value of each LoD to, for example,a header of a bitstream. For example, in the case of the exampleillustrated in FIG. 29, the three-dimensional data encoding device mayadd threshold values Thres_LoD[0], Thres_LoD[1], and Thres_LoD[2] ofrespective LoDs to a header.

Alternatively, the three-dimensional data encoding device may assign allthree-dimensional points which have not yet been assigned to any LoD inthe lowermost-layer LoD. In this case, the three-dimensional dataencoding device is capable of reducing the code amount of the header bynot assigning the threshold value of the lowermost-layer LoD to theheader. For example, in the case of the example illustrated in FIG. 29,the three-dimensional data encoding device assigns threshold valuesThres_LoD[0] and Thres_LoD[1] to the header, and does not assignThres_LoD[2] to the header. In this case, the three-dimensional dataencoding device may estimate value 0 of Thres_LoD[2]. In addition, thethree-dimensional data encoding device may add the number of LoDs to aheader. In this way, the three-dimensional data encoding device iscapable of determining the lowermost-layer LoD using the number of LoDs.

In addition, setting threshold values for the respective layers LoDs insuch a manner that a larger threshold value is set to a higher layermakes a higher layer (layer closer to LoD0) to have a sparse point cloud(sparse) in which three-dimensional points are more distant and makes alower layer to have a dense point cloud (dense) in whichthree-dimensional points are closer. It is to be noted that, in anexample illustrated in FIG. 29, LoD0 is the uppermost layer.

In addition, the method of selecting an initial three-dimensional pointat the time of setting each LoD may depend on an encoding order at thetime of geometry information encoding. For example, thethree-dimensional data encoding device configures LoD0 by selecting thethree-dimensional point encoded first at the time of the geometryinformation encoding as initial point a0 of LoD0, and selecting point a1and point a2 from initial point a0 as the origin. The three-dimensionaldata encoding device then may select the three-dimensional point whosegeometry information has been encoded at the earliest time amongthree-dimensional points which do not belong to LoD0, as initial pointb0 of LoD1. In other words, the three-dimensional data encoding devicemay select the three-dimensional point whose geometry information hasbeen encoded at the earliest time among three-dimensional points whichdo not belong to layers (LoD0 to LoDn−1) above LoDn, as initial point n0of LoDn. In this way, the three-dimensional data encoding device iscapable of configuring the same LoD as in encoding by using, indecoding, the initial point selecting method similar to the one used inthe encoding, which enables appropriate decoding of a bitstream. Morespecifically, the three-dimensional data encoding device selects thethree-dimensional point whose geometry information has been decoded atthe earliest time among three-dimensional points which do not belong tolayers above LoDn, as initial point n0 of LoDn.

Hereinafter, a description is given of a method of generating thepredicted value of the attribute information of each three-dimensionalpoint using information of LoDs. For example, when encodingthree-dimensional points starting with the three-dimensional pointsincluded in LoD0, the three-dimensional data encoding device generatescurrent three-dimensional points which are included in LoD1 usingencoded and decoded (hereinafter also simply referred to as “encoded”)attribute information included in LoD0 and LoD1. In this way, thethree-dimensional data encoding device generates a predicted value ofattribute information of each three-dimensional point included in LoDnusing encoded attribute information included in LoDn′ (n′≤n). In otherwords, the three-dimensional data encoding device does not use attributeinformation of each of three-dimensional points included in any layerbelow LoDn to calculate a predicted value of attribute information ofeach of the three-dimensional points included in LoDn.

For example, the three-dimensional data encoding device calculates anaverage of attribute information of N or less three dimensional pointsamong encoded three-dimensional points surrounding a currentthree-dimensional point to be encoded, to generate a predicted value ofattribute information of the current three-dimensional point. Inaddition, the three-dimensional data encoding device may add value N to,for example, a header of a bitstream. It is to be noted that thethree-dimensional data encoding device may change value N for eachthree-dimensional point, and may add value N for each three-dimensionalpoint. This enables selection of appropriate N for eachthree-dimensional point, which makes it possible to increase theaccuracy of the predicted value. Accordingly, it is possible to reducethe prediction residual. Alternatively, the three-dimensional dataencoding device may add value N to a header of a bitstream, and may fixthe value indicating N in the bitstream. This eliminates the need toencode or decode value N for each three-dimensional point, which makesit possible to reduce the processing amount. In addition, thethree-dimensional data encoding device may encode the values of Nseparately for each LoD. In this way, it is possible to increase thecoding efficiency by selecting appropriate N for each LoD.

Alternatively, the three-dimensional data encoding device may calculatea predicted value of attribute information of three-dimensional pointbased on weighted average values of attribute information of encoded Nneighbor three-dimensional points. For example, the three-dimensionaldata encoding device calculates weights using distance informationbetween a current three-dimensional point and each of N neighborthree-dimensional points.

When encoding value N for each LoD, for example, the three-dimensionaldata encoding device sets larger value N to a higher layer LoD, and setssmaller value N to a lower layer LoD. The distance betweenthree-dimensional points belonging to a higher layer LoD is large, thereis a possibility that it is possible to increase the prediction accuracyby setting large value N, selecting a plurality of neighborthree-dimensional points, and averaging the values. Furthermore, thedistance between three-dimensional points belonging to a lower layer LoDis small, it is possible to perform efficient prediction while reducingthe processing amount of averaging by setting smaller value N.

FIG. 30 is a diagram illustrating an example of attribute information tobe used for predicted values. As described above, the predicted value ofpoint P included in LoDN is generated using encoded neighbor point P′included in LoDN′ (N′≤N). Here, neighbor point P′ is selected based onthe distance from point P. For example, the predicted value of attributeinformation of point b2 illustrated in FIG. 30 is generated usingattribute information of each of points a0, a1, b0, and b1.

Neighbor points to be selected vary depending on the values of Ndescribed above. For example, in the case of N=5, a0, a1, a2, b0, and b1are selected as neighbor points. In the case of N=4, a0, a1, a2, and b1are selected based on distance information.

The predicted value is calculated by distance-dependent weightedaveraging. For example, in the example illustrated in FIG. 30, predictedvalue a2p of point a2 is calculated by weighted averaging of attributeinformation of each of point a0 and a1, as represented by (Equation A2)and (Equation A3). It is to be noted that A_(i) is an attributeinformation value of ai.

[Math.  2] $\begin{matrix}{{a\; 2p} = {\sum\limits_{i = 0}^{1}\;{w_{i} \times A_{i}}}} & \left( {{Equation}\mspace{14mu}{A2}} \right) \\{w_{i} = \frac{\frac{1}{d\left( {{a\; 2},{ai}} \right)}}{\sum\limits_{j = 0}^{1}\;\frac{1}{d\left( {{a\; 2},{aj}} \right)}}} & \left( {{Equation}\mspace{14mu} A\; 3} \right)\end{matrix}$

In addition, predicted value b2p of point b2 is calculated by weightedaveraging of attribute information of each of point a0, a1, a2, b0, andb1, as represented by (Equation A4) and (Equation A6). It is to be notedthat B_(i) is an attribute information value of bi.

[Math.  3] $\begin{matrix}{{b\; 2p} = {{\sum\limits_{i = 0}^{2}\;{{wa}_{i} \times A_{i}}} + {\sum\limits_{i = 0}^{1}\;{{wb}_{i} \times B_{i}}}}} & \left( {{Equation}\mspace{14mu}{A4}} \right) \\{{wa}_{i} = \frac{\frac{1}{d\left( {{b\; 2},{ai}} \right)}}{\sum\limits_{j = 0}^{2}\;\frac{1}{{d\left( {{b\; 2},{aj}} \right)} + {\sum\limits_{j = 0}^{1}\;\frac{1}{d\left( {{b\; 2},{bj}} \right)}}}}} & \left( {{Equation}\mspace{14mu} A\; 5} \right) \\{{wb}_{i} = \frac{\frac{1}{d\left( {{b\; 2},{bi}} \right)}}{{\sum\limits_{j = 0}^{2}\;\frac{1}{d\left( {{b\; 2},{aj}} \right)}} + {\sum\limits_{j = 0}^{1}\;\frac{1}{d\left( {{b\; 2},{bj}} \right)}}}} & \left( {{Equation}\mspace{14mu} A\; 6} \right)\end{matrix}$

In addition, the three-dimensional data encoding device may calculate adifference value (prediction residual) generated from the value ofattribute information of a three-dimensional point and neighbor points,and may quantize the calculated prediction residual. For example, thethree-dimensional data encoding device performs quantization by dividingthe prediction residual by a quantization scale (also referred to as aquantization step). In this case, an error (quantization error) whichmay be generated by quantization reduces as the quantization scale issmaller. In the other case where the quantization scale is larger, theresulting quantization error is larger.

It is to be noted that the three-dimensional data encoding device maychange the quantization scale to be used for each LoD. For example, thethree-dimensional data encoding device reduces the quantization scalemore for a higher layer, and increases the quantization scale more for alower layer. The value of attribute information of a three-dimensionalpoint belonging to a higher layer may be used as a predicted value ofattribute information of a three-dimensional point belonging to a lowerlayer. Thus, it is possible to increase the coding efficiency byreducing the quantization scale for the higher layer to reduce thequantization error that can be generated in the higher layer and toincrease the prediction accuracy of the predicted value. It is to benoted that the three-dimensional data encoding device may add thequantization scale to be used for each LoD to, for example, a header. Inthis way, the three-dimensional data encoding device can decode thequantization scale correctly, thereby appropriately decoding thebitstream.

In addition, the three-dimensional data encoding device may convert asigned integer value (signed quantized value) which is a quantizedprediction residual into an unsigned integer value (unsigned quantizedvalue). This eliminates the need to consider occurrence of a negativeinteger when entropy encoding the prediction residual. It is to be notedthat the three-dimensional data encoding device does not always need toconvert a signed integer value into an unsigned integer value, and, forexample, that the three-dimensional data encoding device may entropyencode a sign bit separately.

The prediction residual is calculated by subtracting a prediction valuefrom the original value. For example, as represented by (Equation A7),prediction residual a2r of point a2 is calculated by subtractingpredicted value a2p of point a2 from value A₂ of attribute informationof point a2. As represented by (Equation A8), prediction residual b2r ofpoint b2 is calculated by subtracting predicted value b2p of point b2from value B₂ of attribute information of point b2.

a2r=A ₂ −a2p  (Equation A7)

b2r=B ₂ −b2p  (Equation A8)

In addition, the prediction residual is quantized by being divided by aQuantization Step (QS). For example, quantized value a2q of point a2 iscalculated according to (Equation A9). Quantized value b2q of point b2is calculated according to (Equation A10). Here, QS_LoD0 is a QS forLoD0, and QS_LoD1 is a QS for LoD1. In other words, a QS may be changedaccording to an LoD.

a2q=a2r/QS_LoD0  (Equation A9)

b2q=b2r/QS_LoD1  (Equation A10)

In addition, the three-dimensional data encoding device converts signedinteger values which are quantized values as indicated below intounsigned integer values as indicated below. When signed integer valuea2q is smaller than 0, the three-dimensional data encoding device setsunsigned integer value a2u to −1−(2×a2q). When signed integer value a2qis 0 or more, the three-dimensional data encoding device sets unsignedinteger value a2u to 2×a2q.

Likewise, when signed integer value b2q is smaller than 0, thethree-dimensional data encoding device sets unsigned integer value b2uto −1−(2×b2q). When signed integer value b2q is 0 or more, thethree-dimensional data encoding device sets unsigned integer value b2uto 2×b2q.

In addition, the three-dimensional data encoding device may encode thequantized prediction residual (unsigned integer value) by entropyencoding. For example, the three-dimensional data encoding device maybinarize the unsigned integer value and then apply binary arithmeticencoding to the binary value.

It is to be noted that, in this case, the three-dimensional dataencoding device may switch binarization methods according to the valueof a prediction residual. For example, when prediction residual pu issmaller than threshold value R_TH, the three-dimensional data encodingdevice binarizes prediction residual pu using a fixed bit count requiredfor representing threshold value R_TH. In addition, when predictionresidual pu is larger than or equal to threshold value R_TH, thethree-dimensional data encoding device binarizes the binary data ofthreshold value R_TH and the value of (pu−R_TH), usingexponential-Golomb coding, or the like.

For example, when threshold value R_TH is 63 and prediction residual puis smaller than 63, the three-dimensional data encoding device binarizesprediction residual pu using 6 bits. When prediction residual pu islarger than or equal to 63, the three-dimensional data encoding deviceperforms arithmetic encoding by binarizing the binary data (111111) ofthreshold value R_TH and (pu−63) using exponential-Golomb coding.

In a more specific example, when prediction residual pu is 32, thethree-dimensional data encoding device generates 6-bit binary data(100000), and arithmetic encodes the bit sequence. In addition, whenprediction residual pu is 66, the three-dimensional data encoding devicegenerates binary data (111111) of threshold value R_TH and a bitsequence (00100) representing value 3 (66−63) using exponential-Golombcoding, and arithmetic encodes the bit sequence (111111+00100).

In this way, the three-dimensional data encoding device can performencoding while preventing a binary bit count from increasing abruptly inthe case where a prediction residual becomes large by switchingbinarization methods according to the magnitude of the predictionresidual. It is to be noted that the three-dimensional data encodingdevice may add threshold value R_TH to, for example, a header of abitstream.

For example, in the case where encoding is performed at a high bit rate,that is, when a quantization scale is small, a small quantization errorand a high prediction accuracy are obtained. As a result, a predictionresidual may not be large. Thus, in this case, the three-dimensionaldata encoding device sets large threshold value R_TH. This reduces thepossibility that the binary data of threshold value R_TH is encoded,which increases the coding efficiency. In the opposite case whereencoding is performed at a low bit rate, that is, when a quantizationscale is large, a large quantization error and a low prediction accuracyare obtained. As a result, a prediction residual may be large. Thus, inthis case, the three-dimensional data encoding device sets smallthreshold value R_TH. In this way, it is possible to prevent abruptincrease in bit length of binary data.

In addition, the three-dimensional data encoding device may switchthreshold value R_TH for each LoD, and may add threshold value R_TH foreach LoD to, for example, a header. In other words, thethree-dimensional data encoding device may switch binarization methodsfor each LoD. For example, since distances between three-dimensionalpoints are large in a higher layer, a prediction accuracy is low, whichmay increase a prediction residual. Thus, the three-dimensional dataencoding device prevents abrupt increase in bit length of binary data bysetting small threshold value R_TH to the higher layer. In addition,since distances between three-dimensional points are small in a lowerlayer, a prediction accuracy is high, which may reduce a predictionresidual. Thus, the three-dimensional data encoding device increases thecoding efficiency by setting large threshold value R_TH to the lowerlayer.

FIG. 31 is a diagram indicating examples of exponential-Golomb codes.The diagram indicates the relationships between pre-binarization values(non-binary values) and post-binarization bits (codes). It is to benoted that 0 and 1 indicated in FIG. 31 may be inverted.

The three-dimensional data encoding device applies arithmetic encodingto the binary data of prediction residuals. In this way, the codingefficiency can be increased. It is to be noted that, in the applicationof the arithmetic encoding, there is a possibility that occurrenceprobability tendencies of 0 and 1 in each bit vary, in binary data,between an n-bit code which is a part binarized by n bits and aremaining code which is a part binarized using exponential-Golombcoding. Thus, the three-dimensional data encoding device may switchmethods of applying arithmetic encoding between the n-bit code and theremaining code.

For example, the three-dimensional data encoding device performsarithmetic encoding on the n-bit code using one or more coding tables(probability tables) different for each bit. At this time, thethree-dimensional data encoding device may change the number of codingtables to be used for each bit. For example, the three-dimensional dataencoding device performs arithmetic encoding using one coding table forfirst bit b0 in an n-bit code. The three-dimensional data encodingdevice uses two coding tables for the next bit b1. The three-dimensionaldata encoding device switches coding tables to be used for arithmeticencoding of bit b1 according to the value (0 or 1) of b0. Likewise, thethree-dimensional data encoding device uses four coding tables for thenext bit b2. The three-dimensional data encoding device switches codingtables to be used for arithmetic encoding of bit b2 according to thevalues (in a range from 0 to 3) of b0 and b1.

In this way, the three-dimensional data encoding device uses 2^(n−1)coding tables when arithmetic encoding each bit bn−1 in n-bit code. Thethree-dimensional data encoding device switches coding tables to be usedaccording to the values (occurrence patterns) of bits before bn−1. Inthis way, the three-dimensional data encoding device can use codingtables appropriate for each bit, and thus can increase the codingefficiency.

It is to be noted that the three-dimensional data encoding device mayreduce the number of coding tables to be used for each bit. For example,the three-dimensional data encoding device may switch 2^(m) codingtables according to the values (occurrence patterns) of m bits (m<n−1)before bn−1 when arithmetic encoding each bit bn−1. In this way, it ispossible to increase the coding efficiency while reducing the number ofcoding tables to be used for each bit. It is to be noted that thethree-dimensional data encoding device may update the occurrenceprobabilities of 0 and 1 in each coding table according to the values ofbinary data occurred actually. In addition, the three-dimensional dataencoding device may fix the occurrence probabilities of 0 and 1 incoding tables for some bit(s). In this way, it is possible to reduce thenumber of updates of occurrence probabilities, and thus to reduce theprocessing amount.

For example, when an n-bit code is b0, b1, b2, . . . , bn−1, the codingtable for b0 is one table (CTb0). Coding tables for b1 are two tables(CTb10 and CTb11). Coding tables to be used are switched according tothe value (0 or 1) of b0. Coding tables for b2 are four tables (CTb20,CTb21, CTb22, and CTb23). Coding tables to be used are switchedaccording to the values (in the range from 0 to 3) of b0 and b1. Codingtables for bn−1 are 2^(n−1) tables (CTbn0, CTbn1, . . . , CTbn(2^(n−1)−1)). Coding tables to be used are switched according to thevalues (in a range from 0 to 2^(n−1)—1) of b0, b1, . . . , bn−2.

It is to be noted that the three-dimensional data encoding device mayapply, to an n-bit code, arithmetic encoding (m=2^(n)) by m-ary thatsets the value in the range from 0 to 2^(n)−1 without binarization. Whenthe three-dimensional data encoding device arithmetic encodes an n-bitcode by an m-ary, the three-dimensional data decoding device mayreconstruct the n-bit code by arithmetic decoding the m-ary.

FIG. 32 is a diagram for illustrating processing in the case whereremaining codes are exponential-Golomb codes. As indicated in FIG. 32,each remaining code which is a part binarized using exponential-Golombcoding includes a prefix and a suffix. For example, thethree-dimensional data encoding device switches coding tables betweenthe prefix and the suffix. In other words, the three-dimensional dataencoding device arithmetic encodes each of bits included in the prefixusing coding tables for the prefix, and arithmetic encodes each of bitsincluded in the suffix using coding tables for the suffix.

It is to be noted that the three-dimensional data encoding device mayupdate the occurrence probabilities of 0 and 1 in each coding tableaccording to the values of binary data occurred actually. In addition,the three-dimensional data encoding device may fix the occurrenceprobabilities of 0 and 1 in one of coding tables. In this way, it ispossible to reduce the number of updates of occurrence probabilities,and thus to reduce the processing amount. For example, thethree-dimensional data encoding device may update the occurrenceprobabilities for the prefix, and may fix the occurrence probabilitiesfor the suffix.

In addition, the three-dimensional data encoding device decodes aquantized prediction residual by inverse quantization andreconstruction, and uses a decoded value which is the decoded predictionresidual for prediction of a current three-dimensional point to beencoded and the following three-dimensional point(s). More specifically,the three-dimensional data encoding device calculates an inversequantized value by multiplying the quantized prediction residual(quantized value) with a quantization scale, and adds the inversequantized value and a prediction value to obtain the decoded value(reconstructed value).

For example, quantized value a2iq of point a2 is calculated usingquantized value a2q of point a2 according to (Equation A11). Inversequantized value b2iq of point b2q is calculated using quantized valueb2q of point b2 according to (Equation A12). Here, QS_LoD0 is a QS forLoD0, and QS_LoD1 is a QS for LoD1. In other words, a QS may be changedaccording to an LoD.

a2iq=a2q×QS_LoD0  (Equation A11)

b2iq=b2q×QS_LoD1  (Equation A12)

For example, as represented by (Equation A13), decoded value a2rec ofpoint a2 is calculated by adding inverse quantization value a2iq ofpoint a2 to predicted value a2p of point a2. As represented by (EquationA14), decoded value b2rec of point b2 is calculated by adding inversequantized value b2iq of point b2 to predicted value b2p of point b2.

a2rec=a2iq+a2p  (Equation A13)

b2rec=b2iq+b2p  (Equation A14)

Hereinafter, a syntax example of a bitstream according to the presentembodiment is described. FIG. 33 is a diagram indicating the syntaxexample of an attribute header (attribute_header) according to thepresent embodiment. The attribute header is header information ofattribute information. As indicated in FIG. 33, the attribute headerincludes the number of layers information (NumLoD), the number ofthree-dimensional points information (NumOfPoint[i]), a layer thresholdvalue (Thres_LoD[i]), the number of neighbor points information(NumNeighborPoint[i]), a prediction threshold value (THd[i]), aquantization scale (QS[i]), and a binarization threshold value(R_TH[i]).

The number of layers information (NumLoD) indicates the number of LoDsto be used.

The number of three-dimensional points information (NumOfPoint[i])indicates the number of three-dimensional points belonging to layer i.It is to be noted that the three-dimensional data encoding device mayadd, to another header, the number of three-dimensional pointsinformation indicating the total number of three-dimensional points. Inthis case, the three-dimensional data encoding device does not need toadd, to a header, NumOfPoint [NumLoD−1] indicating the number ofthree-dimensional points belonging to the lowermost layer. In this case,the three-dimensional data decoding device is capable of calculatingNumOfPoint [NumLoD−1] according to (Equation A15). In this case, it ispossible to reduce the code amount of the header.

     [Math.  4] $\begin{matrix}{{{NumOfPoint}\left\lbrack {{NumLoD} - 1} \right\rbrack} = {{AllNumOfPoint} - {\sum\limits_{j = 0}^{{NumLoD} - 2}\;{{NumOfPoint}\lbrack j\rbrack}}}} & \left( {{Equation}\mspace{14mu}{A15}} \right)\end{matrix}$

The layer threshold value (Thres_LoD[i]) is a threshold value to be usedto set layer i. The three-dimensional data encoding device and thethree-dimensional data decoding device configure LoDi in such a mannerthat the distance between points in LoDi becomes larger than thresholdvalue Thres_LoD[i]. The three-dimensional data encoding device does notneed to add the value of Thres_LoD [NumLoD−1] (lowermost layer) to aheader. In this case, the three-dimensional data decoding device mayestimate 0 as the value of Thres_LoD [NumLoD−1]. In this case, it ispossible to reduce the code amount of the header.

The number of neighbor points information (NumNeighborPoint[i])indicates the upper limit value of the number of neighbor points to beused to generate a predicted value of a three-dimensional pointbelonging to layer i. The three-dimensional data encoding device maycalculate a predicted value using the number of neighbor points M whenthe number of neighbor points M is smaller than NumNeighborPoint[i](M<NumNeighborPoint[i]). Furthermore, when there is no need todifferentiate the values of NumNeighborPoint[i] for respective LoDs, thethree-dimensional data encoding device may add a piece of the number ofneighbor points information (NumNeighborPoint) to be used in all LoDs toa header.

The prediction threshold value (THd[i]) indicates the upper limit valueof the distance between a current three-dimensional point to be encodedor decoded in layer i and each of neighbor three-dimensional points tobe used to predict the current three-dimensional point. Thethree-dimensional data encoding device and the three-dimensional datadecoding device do not use, for prediction, any three-dimensional pointdistant from the current three-dimensional point over THd[i]. It is tobe noted that, when there is no need to differentiate the values ofTHd[i] for respective LoDs, the three-dimensional data encoding devicemay add a single prediction threshold value (THd) to be used in all LoDsto a header.

The quantization scale (QS[i]) indicates a quantization scale to be usedfor quantization and inverse quantization in layer i.

The binarization threshold value (R_TH[i]) is a threshold value forswitching binarization methods of prediction residuals ofthree-dimensional points belonging to layer i. For example, thethree-dimensional data encoding device binarizes prediction residual puusing a fixed bit count when a prediction residual is smaller thanthreshold value R_TH, and binarizes the binary data of threshold valueR_TH and the value of (pu−R_TH) using exponential-Golomb coding when aprediction residual is larger than or equal to threshold value R_TH. Itis to be noted that, when there is no need to switch the values ofR_TH[i] between LoDs, the three-dimensional data encoding device may adda single binarization threshold value (R_TH) to be used in all LoDs to aheader.

It is to be noted that R_TH[i] may be the maximum value which can berepresented by n bits. For example, R_TH is 63 in the case of 6 bits,and R_TH is 255 in the case of 8 bits. Alternatively, thethree-dimensional data encoding device may encode a bit count instead ofencoding the maximum value which can be represented by n bits as abinarization threshold value. For example, the three-dimensional dataencoding device may add value 6 in the case of R_TH[i]=63 to a header,and may add value 8 in the case of R_TH[i]=255 to a header.Alternatively, the three-dimensional data encoding device may define theminimum value (minimum bit count) representing R_TH[i], and add arelative bit count from the minimum value to a header. For example, thethree-dimensional data encoding device may add value 0 to a header whenR_TH[i]=63 is satisfied and the minimum bit count is 6, and may addvalue 2 to a header when R_TH[i]=255 is satisfied and the minimum bitcount is 6.

Alternatively, the three-dimensional data encoding device may entropyencode at least one of NumLoD, Thres_LoD[i], NumNeighborPoint[i],THd[i], QS[i], and R_TH[i], and add the entropy encoded one to a header.For example, the three-dimensional data encoding device may binarizeeach value and perform arithmetic encoding on the binary value. Inaddition, the three-dimensional data encoding device may encode eachvalue using a fixed length in order to reduce the processing amount.

Alternatively, the three-dimensional data encoding device does notalways need to add at least one of NumLoD, Thres_LoD[i],NumNeighborPoint[i], THd[i], QS[i], and R_TH[i] to a header. Forexample, at least one of these values may be defined by a profile or alevel in a standard, or the like. In this way, it is possible to reducethe bit amount of the header.

FIG. 34 is a diagram indicating the syntax example of attribute data(attribute_data) according to the present embodiment. The attribute dataincludes encoded data of the attribute information of a plurality ofthree-dimensional points. As indicated in FIG. 34, the attribute dataincludes an n-bit code and a remaining code.

The n-bit code is encoded data of a prediction residual of a value ofattribute information or a part of the encoded data. The bit length ofthe n-bit code depends on value R_TH[i]. For example, the bit length ofthe n-bit code is 6 bits when the value indicated by R_TH[i] is 63, thebit length of the n-bit code is 8 bits when the value indicated byR_TH[i] is 255.

The remaining code is encoded data encoded using exponential-Golombcoding among encoded data of the prediction residual of the value of theattribute information. The remaining code is encoded or decoded when thevalue of the n-bit code is equal to R_TH[i]. The three-dimensional datadecoding device decodes the prediction residual by adding the value ofthe n-bit code and the value of the remaining code. It is to be notedthat the remaining code does not always need to be encoded or decodedwhen the value of the n-bit code is not equal to R_TH[i].

Hereinafter, a description is given of a flow of processing in thethree-dimensional data encoding device. FIG. 35 is a flowchart of athree-dimensional data encoding process performed by thethree-dimensional data encoding device.

First, the three-dimensional data encoding device encodes geometryinformation (geometry) (S3001). For example, the three-dimensional dataencoding is performed using octree representation.

When the positions of three-dimensional points changed by quantization,etc., after the encoding of the geometry information, thethree-dimensional data encoding device re-assigns attribute informationof the original three-dimensional points to the post-changethree-dimensional points (S3002). For example, the three-dimensionaldata encoding device interpolates values of attribute informationaccording to the amounts of change in position to re-assign theattribute information. For example, the three-dimensional data encodingdevice detects pre-change N three-dimensional points closer to thepost-change three-dimensional positions, and performs weighted averagingof the values of attribute information of the N three-dimensionalpoints. For example, the three-dimensional data encoding devicedetermines weights based on distances from the post-changethree-dimensional positions to the respective N three-dimensionalpositions in weighted averaging. The three-dimensional data encodingdevice then determines the values obtained through the weightedaveraging to be the values of the attribute information of thepost-change three-dimensional points. When two or more of thethree-dimensional points are changed to the same three-dimensionalposition through quantization, etc., the three-dimensional data encodingdevice may assign the average value of the attribute information of thepre-change two or more three-dimensional points as the values of theattribute information of the post-change three-dimensional points.

Next, the three-dimensional data encoding device encodes the attributeinformation (attribute) re-assigned (S3003). For example, when encodinga plurality of kinds of attribute information, the three-dimensionaldata encoding device may encode the plurality of kinds of attributeinformation in order. For example, when encoding colors and reflectancesas attribute information, the three-dimensional data encoding device maygenerate a bitstream added with the color encoding results and thereflectance encoding results after the color encoding results. It is tobe noted that the order of the plurality of encoding results ofattribute information to be added to a bitstream is not limited to theorder, and may be any order.

Alternatively, the three-dimensional data encoding device may add, to aheader for example, information indicating the start location of encodeddata of each attribute information in a bitstream. In this way, thethree-dimensional data decoding device is capable of selectivelydecoding attribute information required to be decoded, and thus iscapable of skipping the decoding process of the attribute informationnot required to be decoded. Accordingly, it is possible to reduce theamount of processing by the three-dimensional data decoding device.Alternatively, the three-dimensional data encoding device may encode aplurality of kinds of attribute information in parallel, and mayintegrate the encoding results into a single bitstream. In this way, thethree-dimensional data encoding device is capable of encoding theplurality of kinds of attribute information at high speed.

FIG. 36 is a flowchart of an attribute information encoding process(S3003). First, the three-dimensional data encoding device sets LoDs(S3011). In other words, the three-dimensional data encoding deviceassigns each of three-dimensional points to any one of the plurality ofLoDs.

Next, the three-dimensional data encoding device starts a loop for eachLoD (S3012). In other words, the three-dimensional data encoding deviceiteratively performs the processes of Steps from S3013 to S3021 for eachLoD.

Next, the three-dimensional data encoding device starts a loop for eachthree-dimensional point (S3013). In other words, the three-dimensionaldata encoding device iteratively performs the processes of Steps fromS3014 to S3020 for each three-dimensional point.

First, the three-dimensional data encoding device searches a pluralityof neighbor points which are three-dimensional points present in theneighborhood of a current three-dimensional point to be processed andare to be used to calculate a predicted value of the currentthree-dimensional point (S3014). Next, the three-dimensional dataencoding device calculates the weighted average of the values ofattribute information of the plurality of neighbor points, and sets theresulting value to predicted value P (S3015). Next, thethree-dimensional data encoding device calculates a prediction residualwhich is the difference between the attribute information of the currentthree-dimensional point and the predicted value (S3016). Next, thethree-dimensional data encoding device quantizes the prediction residualto calculate a quantized value (S3017). Next, the three-dimensional dataencoding device arithmetic encodes the quantized value (S3018).

Next, the three-dimensional data encoding device inverse quantizes thequantized value to calculate an inverse quantized value (S3019). Next,the three-dimensional data encoding device adds a prediction value tothe inverse quantized value to generate a decoded value (S3020). Next,the three-dimensional data encoding device ends the loop for eachthree-dimensional point (S3021). Next, the three-dimensional dataencoding device ends the loop for each LoD (S3022).

Hereinafter, a description is given of a three-dimensional data decodingprocess in the three-dimensional data decoding device which decodes abitstream generated by the three-dimensional data encoding device.

The three-dimensional data decoding device generates decoded binary databy arithmetic decoding the binary data of the attribute information inthe bitstream generated by the three-dimensional data encoding device,according to the method similar to the one performed by thethree-dimensional data encoding device. It is to be noted that whenmethods of applying arithmetic encoding are switched between the part(n-bit code) binarized using n bits and the part (remaining code)binarized using exponential-Golomb coding in the three-dimensional dataencoding device, the three-dimensional data decoding device performsdecoding in conformity with the arithmetic encoding, when applyingarithmetic decoding.

For example, the three-dimensional data decoding device performsarithmetic decoding using coding tables (decoding tables) different foreach bit in the arithmetic decoding of the n-bit code. At this time, thethree-dimensional data decoding device may change the number of codingtables to be used for each bit. For example, the three-dimensional datadecoding device performs arithmetic decoding using one coding table forfirst bit b0 in the n-bit code. The three-dimensional data decodingdevice uses two coding tables for the next bit b1. The three-dimensionaldata decoding device switches coding tables to be used for arithmeticdecoding of bit b1 according to the value (0 or 1) of b0. Likewise, thethree-dimensional data decoding device uses four coding tables for thenext bit b2. The three-dimensional data decoding device switches codingtables to be used for arithmetic decoding of bit b2 according to thevalues (in the range from 0 to 3) of b0 and b1.

In this way, the three-dimensional data decoding device uses 2^(n−1)coding tables when arithmetic decoding each bit bn−1 in the n-bit code.The three-dimensional data decoding device switches coding tables to beused according to the values (occurrence patterns) of bits before bn−1.In this way, the three-dimensional data decoding device is capable ofappropriately decoding a bitstream encoded at an increased codingefficiency using the coding tables appropriate for each bit.

It is to be noted that the three-dimensional data decoding device mayreduce the number of coding tables to be used for each bit. For example,the three-dimensional data decoding device may switch 2^(m) codingtables according to the values (occurrence patterns) of m bits (m<n−1)before bn−1 when arithmetic decoding each bit bn−1. In this way, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream encoded at the increased coding efficiency whilereducing the number of coding tables to be used for each bit. It is tobe noted that the three-dimensional data decoding device may update theoccurrence probabilities of 0 and 1 in each coding table according tothe values of binary data occurred actually. In addition, thethree-dimensional data decoding device may fix the occurrenceprobabilities of 0 and 1 in coding tables for some bit(s). In this way,it is possible to reduce the number of updates of occurrenceprobabilities, and thus to reduce the processing amount.

For example, when an n-bit code is b0, b1, b2, . . . , bn−1, the codingtable for b0 is one (CTb0). Coding tables for b1 are two tables (CTb10and CTb11). Coding tables to be used are switched according to the value(0 or 1) of b0. Coding tables for b2 are four tables (CTb20, CTb21,CTb22, and CTb23). Coding tables to be used according to the values (inthe range from 0 to 3) of b0 and b1. Coding tables for bn−1 are 2^(n−1)tables (CTbn0, CTbn1, . . . , CTbn (2^(n−1)−1)). Coding tables to beused are switched according to the values (in the range from 0 to2^(n−1)−1) of b0, b1, . . . , bn−2.

FIG. 37 is a diagram for illustrating processing in the case whereremaining codes are exponential-Golomb codes. As indicated in FIG. 37,the part (remaining part) binarized and encoded by the three-dimensionaldata encoding device using exponential-Golomb coding includes a prefixand a suffix. For example, the three-dimensional data decoding deviceswitches coding tables between the prefix and the suffix. In otherwords, the three-dimensional data decoding device arithmetic decodeseach of bits included in the prefix using coding tables for the prefix,and arithmetic decodes each of bits included in the suffix using codingtables for the suffix.

It is to be noted that the three-dimensional data decoding device mayupdate the occurrence probabilities of 0 and 1 in each coding tableaccording to the values of binary data occurred at the time of decoding.In addition, the three-dimensional data decoding device may fix theoccurrence probabilities of 0 and 1 in one of coding tables. In thisway, it is possible to reduce the number of updates of occurrenceprobabilities, and thus to reduce the processing amount. For example,the three-dimensional data decoding device may update the occurrenceprobabilities for the prefix, and may fix the occurrence probabilitiesfor the suffix.

Furthermore, the three-dimensional data decoding device decodes thequantized prediction residual (unsigned integer value) by debinarizingthe binary data of the prediction residual arithmetic decoded accordingto a method in conformity with the encoding method used by thethree-dimensional data encoding device. The three-dimensional datadecoding device first arithmetic decodes the binary data of an n-bitcode to calculate a value of the n-bit code. Next, the three-dimensionaldata decoding device compares the value of the n-bit code with thresholdvalue R_TH.

In the case where the value of the n-bit code and threshold value R_THmatch, the three-dimensional data decoding device determines that a bitencoded using exponential-Golomb coding is present next, and arithmeticdecodes the remaining code which is the binary data encoded usingexponential-Golomb coding. The three-dimensional data decoding devicethen calculates, from the decoded remaining code, a value of theremaining code using a reverse lookup table indicating the relationshipbetween the remaining code and the value. FIG. 38 is a diagramindicating an example of a reverse lookup table indicating relationshipsbetween remaining codes and the values thereof. Next, thethree-dimensional data decoding device adds the obtained value of theremaining code to R_TH, thereby obtaining a debinarized quantizedprediction residual.

In the opposite case where the value of the n-bit code and thresholdvalue R_TH do not match (the value of the n-bit code is smaller thanvalue R_TH), the three-dimensional data decoding device determines thevalue of the n-bit code to be the debinarized quantized predictionresidual as it is. In this way, the three-dimensional data decodingdevice is capable of appropriately decoding the bitstream generatedwhile switching the binarization methods according to the values of theprediction residuals by the three-dimensional data encoding device.

It is to be noted that, when threshold value R_TH is added to, forexample, a header of a bitstream, the three-dimensional data decodingdevice may decode threshold value R_TH from the header, and may switchdecoding methods using decoded threshold value R_TH. When thresholdvalue R_TH is added to, for example, a header for each LoD, thethree-dimensional data decoding device switch decoding methods usingthreshold value R_TH decoded for each LoD.

For example, when threshold value R_TH is 63 and the value of thedecoded n-bit code is 63, the three-dimensional data decoding devicedecodes the remaining code using exponential-Golomb coding, therebyobtaining the value of the remaining code. For example, in the exampleindicated in FIG. 38, the remaining code is 00100, and 3 is obtained asthe value of the remaining code. Next, the three-dimensional datadecoding device adds 63 that is threshold value R_TH and 3 that is thevalue of the remaining code, thereby obtaining 66 that is the value ofthe prediction residual.

In addition, when the value of the decoded n-bit code is 32, thethree-dimensional data decoding device sets 32 that is the value of then-bit code to the value of the prediction residual.

In addition, the three-dimensional data decoding device converts thedecoded quantized prediction residual, for example, from an unsignedinteger value to a signed integer value, through processing inverse tothe processing in the three-dimensional data encoding device. In thisway, when entropy decoding the prediction residual, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream generated without considering occurrence of anegative integer. It is to be noted that the three-dimensional datadecoding device does not always need to convert an unsigned integervalue to a signed integer value, and that, for example, thethree-dimensional data decoding device may decode a sign bit whendecoding a bitstream generated by separately entropy encoding the signbit.

The three-dimensional data decoding device performs decoding by inversequantizing and reconstructing the quantized prediction residual afterbeing converted to the signed integer value, to obtain a decoded value.The three-dimensional data decoding device uses the generated decodedvalue for prediction of a current three-dimensional point to be decodedand the following three-dimensional point(s). More specifically, thethree-dimensional data decoding device multiplies the quantizedprediction residual by a decoded quantization scale to calculate aninverse quantized value and adds the inverse quantized value and thepredicted value to obtain the decoded value.

The decoded unsigned integer value (unsigned quantized value) isconverted into a signed integer value through the processing indicatedbelow. When the least significant bit (LSB) of decoded unsigned integervalue a2u is 1, the three-dimensional data decoding device sets signedinteger value a2q to −((a2u+1)>>1). When the LSB of unsigned integervalue a2u is not 1, the three-dimensional data decoding device setssigned integer value a2q to ((a2u>>1).

Likewise, when an LSB of decoded unsigned integer value b2u is 1, thethree-dimensional data decoding device sets signed integer value b2q to−((b2u+1)>>1). When the LSB of decoded unsigned integer value n2u is not1, the three-dimensional data decoding device sets signed integer valueb2q to ((b2u>>1).

Details of the inverse quantization and reconstruction processing by thethree-dimensional data decoding device are similar to the inversequantization and reconstruction processing in the three-dimensional dataencoding device.

Hereinafter, a description is given of a flow of processing in thethree-dimensional data decoding device. FIG. 39 is a flowchart of athree-dimensional data decoding process performed by thethree-dimensional data decoding device. First, the three-dimensionaldata decoding device decodes geometry information (geometry) from abitstream (S3031). For example, the three-dimensional data decodingdevice performs decoding using octree representation.

Next, the three-dimensional data decoding device decodes attributeinformation (attribute) from the bitstream (S3032). For example, whendecoding a plurality of kinds of attribute information, thethree-dimensional data decoding device may decode the plurality of kindsof attribute information in order. For example, when decoding colors andreflectances as attribute information, the three-dimensional datadecoding device decodes the color encoding results and the reflectanceencoding results in order of assignment in the bitstream. For example,when the reflectance encoding results are added after the color encodingresults in a bitstream, the three-dimensional data decoding devicedecodes the color encoding results, and then decodes the reflectanceencoding results. It is to be noted that the three-dimensional datadecoding device may decode, in any order, the encoding results of theattribute information added to the bitstream.

Alternatively, the three-dimensional data encoding device may add, to aheader for example, information indicating the start location of encodeddata of each attribute information in a bitstream. In this way, thethree-dimensional data decoding device is capable of selectivelydecoding attribute information required to be decoded, and thus iscapable of skipping the decoding process of the attribute informationnot required to be decoded. Accordingly, it is possible to reduce theamount of processing by the three-dimensional data decoding device. Inaddition, the three-dimensional data decoding device may decode aplurality of kinds of attribute information in parallel, and mayintegrate the decoding results into a single three-dimensional pointcloud. In this way, the three-dimensional data decoding device iscapable of decoding the plurality of kinds of attribute information athigh speed.

FIG. 40 is a flowchart of an attribute information decoding process(S3032). First, the three-dimensional data decoding device sets LoDs(S3041). In other words, the three-dimensional data decoding deviceassigns each of three-dimensional points having the decoded geometryinformation to any one of the plurality of LoDs. For example, thisassignment method is the same as the assignment method used in thethree-dimensional data encoding device.

Next, the three-dimensional data decoding device starts a loop for eachLoD (S3042). In other words, the three-dimensional data decoding deviceiteratively performs the processes of Steps from S3043 to S3049 for eachLoD.

Next, the three-dimensional data decoding device starts a loop for eachthree-dimensional point (S3043). In other words, the three-dimensionaldata decoding device iteratively performs the processes of Steps fromS3044 to S3048 for each three-dimensional point.

First, the three-dimensional data decoding device searches a pluralityof neighbor points which are three-dimensional points present in theneighborhood of a current three-dimensional point to be processed andare to be used to calculate a predicted value of the currentthree-dimensional point to be processed (S3044). Next, thethree-dimensional data decoding device calculates the weighted averageof the values of attribute information of the plurality of neighborpoints, and sets the resulting value to predicted value P (S3045). It isto be noted that these processes are similar to the processes in thethree-dimensional data encoding device.

Next, the three-dimensional data decoding device arithmetic decodes thequantized value from the bitstream (S3046). The three-dimensional datadecoding device inverse quantizes the decoded quantized value tocalculate an inverse quantized value (S3047). Next, thethree-dimensional data decoding device adds a predicted value to theinverse quantized value to generate a decoded value (S3048). Next, thethree-dimensional data decoding device ends the loop for eachthree-dimensional point (S3049). Next, the three-dimensional dataencoding device ends the loop for each LoD (S3050).

The following describes configurations of the three-dimensional dataencoding device and three-dimensional data decoding device according tothe present embodiment. FIG. 41 is a block diagram illustrating aconfiguration of three-dimensional data encoding device 3000 accordingto the present embodiment. Three-dimensional data encoding device 3000includes geometry information encoder 3001, attribute informationre-assigner 3002, and attribute information encoder 3003.

Attribute information encoder 3003 encodes geometry information(geometry) of a plurality of three-dimensional points included in aninput point cloud. Attribute information re-assigner 3002 re-assigns thevalues of attribute information of the plurality of three-dimensionalpoints included in the input point cloud, using the encoding anddecoding results of the geometry information. Attribute informationencoder 3003 encodes the re-assigned attribute information (attribute).Furthermore, three-dimensional data encoding device 3000 generates abitstream including the encoded geometry information and the encodedattribute information.

FIG. 42 is a block diagram illustrating a configuration ofthree-dimensional data decoding device 3010 according to the presentembodiment. Three-dimensional data decoding device 3010 includesgeometry information decoder 3011 and attribute information decoder3012.

Geometry information decoder 3011 decodes the geometry information(geometry) of a plurality of three-dimensional points from a bitstream.Attribute information decoder 3012 decodes the attribute information(attribute) of the plurality of three-dimensional points from thebitstream. Furthermore, three-dimensional data decoding device 3010integrates the decoded geometry information and the decoded attributeinformation to generate an output point cloud.

Embodiment 8

Hereinafter, a method using a RAHT (Region Adaptive HierarchicalTransform) will be described as another method of encoding the attributeinformation of a three-dimensional point. FIG. 43 is a diagram fordescribing the encoding of the attribute information by using a RAHT.

First, the three-dimensional data encoding device generates Morton codesbased on the geometry information of three-dimensional points, and sortsthe attribute information of the three-dimensional points in the orderof the Morton codes. For example, the three-dimensional data encodingdevice may perform sorting in the ascending order of the Morton codes.Note that the sorting order is not limited to the order of the Mortoncodes, and other orders may be used.

Next, the three-dimensional data encoding device generates ahigh-frequency component and a low-frequency component of the layer L byapplying the Haar conversion to the attribute information of twoadjacent three-dimensional points in the order of the Morton codes. Forexample, the three-dimensional data encoding device may use the Haarconversion of 2×2 matrices. The generated high-frequency component isincluded in a coding coefficient as the high-frequency component of thelayer L, and the generated low-frequency component is used as the inputvalue for the higher layer L+1 of the layer L.

After generating the high-frequency component of the layer L by usingthe attribute information of the layer L, the three-dimensional dataencoding device subsequently performs processing of the layer L+1. Inthe processing of the layer L+1, the three-dimensional data encodingdevice generates a high-frequency component and a low-frequencycomponent of the layer L+1 by applying the Haar conversion to twolow-frequency components obtained by the Haar conversion of theattribute information of the layer L. The generated high-frequencycomponent is included in a coding coefficient as the high-frequencycomponent of the layer L+1, and the generated low-frequency component isused as the input value for the higher layer L+2 of the layer L+1.

The three-dimensional data encoding device repeats such layerprocessing, and determines that the highest layer Lmax has been reachedat the time when a low-frequency component that is input to a layerbecomes one. The three-dimensional data encoding device includes thelow-frequency component of the layer Lmax-1 that is input to the LayerLmax in a coding coefficient. Then, the value of the low-frequencycomponent or high-frequency component included in the coding coefficientis quantized, and is encoded by using entropy encoding or the like.

Note that, when only one three-dimensional point exists as two adjacentthree-dimensional points at the time of application of the Haarconversion, the three-dimensional data encoding device may use the valueof the attribute information of the existing one three-dimensional pointas the input value for a higher layer.

In this manner, the three-dimensional data encoding devicehierarchically applies the Haar conversion to the input attributeinformation, generates a high-frequency component and a low-frequencycomponent of the attribute information, and performs encoding byapplying quantization described later or the like. Accordingly, thecoding efficiency can be improved.

When the attribute information is N dimensional, the three-dimensionaldata encoding device may independently apply the Haar conversion foreach dimension, and may calculate each coding coefficient. For example,when the attribute information is color information (RGB, YUV, or thelike), the three-dimensional data encoding device applies the Haarconversion for each component, and calculates each coding coefficient.

The three-dimensional data encoding device may apply the Haar conversionin the order of the layers L, L+1, . . . , Lmax. The closer to the layerLmax, a coding coefficient including the more low-frequency componentsof the input attribute information is generated.

w0 and w1 shown in FIG. 43 are the weights assigned to eachthree-dimensional point. For example, the three-dimensional dataencoding device may calculate the weight based on the distanceinformation between two adjacent three-dimensional points to which theHaar conversion is applied, or the like. For example, thethree-dimensional data encoding device may improve the coding efficiencysuch that the closer the distance, the greater the weight. Note that thethree-dimensional data encoding device may calculate this weight withanother technique, or need not use the weight.

In the example shown in FIG. 43, the pieces of the input attributeinformation are a0, a1, a2, a3, a4, and a5. Additionally, Ta1, Ta5, Tb1,Tb3, Tc1, and d0 are encoded among the coding coefficients after theHaar conversion. The other coding coefficients (b0, b2, c0 and the like)are medians, and are not encoded.

Specifically, in the example shown in FIG. 43, the high-frequencycomponent Ta1 and the low-frequency component b0 are generated byperforming the Haar conversion on a0 and a1. Here, when the weights w0and w1 are equal, the low-frequency component b0 is the average value ofa0 and a1, and the high-frequency component Ta1 is the differencebetween a0 and a1.

Since there is no attribute information to be paired with a2, a2 is usedas b1 as is. Similarly, since there is no attribute information to bepaired with a3, a3 is used as b2 as is. Additionally, the high-frequencycomponent Ta5 and the low-frequency component b3 are generated byperforming the Haar conversion on a4 and a5.

In the layer L+1, the high-frequency component Tb1 and the low-frequencycomponent c0 are generated by performing the Haar conversion on b0 andb1. Similarly, the high-frequency component Tb3 and the low-frequencycomponent c1 are generated by performing the Haar conversion on b2 andb3.

In the layer Lmax−1, the High-frequency component Tc1 and thelow-frequency component d0 are generated by performing the Haarconversion on c0 and c1.

The three-dimensional data encoding device may encode the codingcoefficients to which the Haar conversion has been applied, afterquantizing the coding coefficients. For example, the three-dimensionaldata encoding device performs quantization by dividing the codingcoefficient by the quantization scale (also called the quantization step(QS)). In this case, the smaller the quantization scale, the smaller theerror (quantization error) that may occur due to quantization.Conversely, the larger the quantization scale, the larger thequantization error.

Note that the three-dimensional data encoding device may change thevalue of the quantization scale for each layer. FIG. 44 is a diagramshowing an example of setting the quantization scale for each layer. Forexample, the three-dimensional data encoding device sets smallerquantization scales to the higher layers, and larger quantization scalesto the lower layers. Since the coding coefficients of thethree-dimensional points belonging to the higher layers include morelow-frequency components than the lower layers, there is a highpossibility that the coding coefficients are important components inhuman visual characteristics and the like. Therefore, by suppressing thequantization error that may occur in the higher layers by making thequantization scales for the higher layers small, visual deteriorationcan be suppressed, and the coding efficiency can be improved.

Note that the three-dimensional data encoding device may add thequantization scale for each layer to a header or the like. Accordingly,the three-dimensional decoding device can correctly decode thequantization scale, and can appropriately decode a bitstream.

Additionally, the three-dimensional data encoding device may adaptivelyswitch the value of the quantization scale according to the importanceof a current three-dimensional point to be encoded. For example, thethree-dimensional data encoding device uses a small quantization scalefor a three-dimensional point with high importance, and uses a largequantization scale for a three-dimensional point with low importance.For example, the three-dimensional data encoding device may calculatethe importance from the weight at the time of the Haar conversion, orthe like. For example, the three-dimensional data encoding device maycalculate the quantization scale by using the sum of w0 and w1. In thismanner, by making the quantization scale of a three-dimensional pointwith high importance small, the quantization error becomes small, andthe coding efficiency can be improved.

Additionally, the value of the QS may be made smaller for the higherlayers. Accordingly, the higher the layer, the larger the value of theQW, and the prediction efficiency can be improved by suppressing thequantization error of the three-dimensional point.

Here, a coding coefficient Ta1q after quantization of the codingcoefficient Ta1 of the attribute information a1 is represented byTa1/QS_L. Note that QS may be the same value in all the layers or a partof the layers.

The QW (Quantization Weight) is the value that represents the importanceof a current three-dimensional point to be encoded. For example, theabove-described sum of w0 and w1 may be used as the QW. Accordingly, thehigher the layer, the larger the value of the QW, and the predictionefficiency can be improved by suppressing the quantization error of thethree-dimensional point.

For example, the three-dimensional data encoding device may firstinitialize the values of the QWs of all the three-dimensional pointswith 1, and may update the QW of each three-dimensional point by usingthe values of w0 and w1 at the time of the Haar conversion.Alternatively, the three-dimensional data encoding device may change theinitial value according to the layers, without initializing the valuesof the QWs of all the three-dimensional points with a value of 1. Forexample, the quantization scales for the higher layers becomes small bysetting larger QW initial values for the higher layers. Accordingly,since the prediction error in the higher layers can be suppressed, theprediction accuracy of the lower layers can be increased, and the codingefficiency can be improved. Note that the three-dimensional dataencoding device need not necessarily use the QW.

When using the QW, the quantized value Ta1q of Ta1 is calculated by(Formula K1) and (Formula K2).

[Math.  5] $\begin{matrix}{{{Ta}\; 1q} = {\frac{{{Ta}\; 1} + \frac{QS\_ L}{2}}{QS\_ LoD1} \times {QWTa}\; 1}} & \left( {{Formula}\mspace{14mu}{K1}} \right) \\{{{QWTa}\; 1} = {1 + {\sum\limits_{i = 0}^{1}\; w_{i}}}} & \left( {{Formula}\mspace{14mu} K\; 2} \right)\end{matrix}$

Additionally, the three-dimensional data encoding device scans andencodes the coding coefficients (unsigned integer values) afterquantization in a certain order. For example, the three-dimensional dataencoding device encodes a plurality of three-dimensional points from thethree-dimensional points included in the higher layers toward the lowerlayers in order.

For example, in the example shown in FIG. 43, the three-dimensional dataencoding device encodes a plurality of three-dimensional points in theorder of Tc1q Tb1q, Tb3q, Ta1q, and Ta5q from d0q included in the higherlayer Lmax. Here, there is a tendency that the lower the layer L, themore likely it is that the coding coefficient after quantization becomes0. This can be due to the following and the like.

Since the coding coefficient of the lower layer L shows a higherfrequency component than the higher layers, there is a tendency that thecoding coefficient becomes 0 depending on a current three-dimensionalpoint. Additionally, by switching the quantization scale according tothe above-described importance or the like, the lower the layer, thelarger the quantization scales, and the more likely it is that thecoding coefficient after quantization becomes 0.

In this manner, the lower the layer, the more likely it is that thecoding coefficient after quantization becomes 0, and the value 0consecutively occurs in the first code sequence. FIG. 45 is a diagramshowing an example of the first code sequence and the second codesequence.

The three-dimensional data encoding device counts the number of timesthat the value 0 occurs in the first code sequence, and encodes thenumber of times that the value 0 consecutively occurs, instead of theconsecutive values 0. That is, the three-dimensional data encodingdevice generates a second code sequence by replacing the codingcoefficient of the consecutive values 0 in the first code sequence withthe number of consecutive times (ZeroCnt) of 0. Accordingly, when thereare consecutive values 0 of the coding coefficients after quantization,the coding efficiency can be improved by encoding the number ofconsecutive times of 0, rather than encoding a lot of 0s.

Additionally, the three-dimensional data encoding device may entropyencode the value of ZeroCnt. For example, the three-dimensional dataencoding device binarizes the value of ZeroCnt with the truncated unarycode of the total number T of the encoded three-dimensional points, andarithmetically encodes each bit after the binarization. FIG. 46 is adiagram showing an example of the truncated unary code in the case wherethe total number of encoded three-dimensional points is T. At this time,the three-dimensional data encoding device may improve the codingefficiency by using a different coding table for each bit. For example,the three-dimensional data encoding device uses coding table 1 for thefirst bit, uses coding table 2 for the second bit, and coding table 3for the subsequent bits. In this manner, the three-dimensional dataencoding device can improve the coding efficiency by switching thecoding table for each bit.

Additionally, the three-dimensional data encoding device mayarithmetically encode ZeroCnt after binarizing ZeroCnt with anExponential-Golomb. Accordingly, when the value of ZeroCnt easilybecomes large, the efficiency can be more improved than the binarizedarithmetic encoding with the truncated unary code. Note that thethree-dimensional data encoding device may add a flag for switchingbetween using the truncated unary code and using the Exponential-Golombto a header. Accordingly, the three-dimensional data encoding device canimprove the coding efficiency by selecting the optimum binarizationmethod. Additionally, the three-dimensional data decoding device cancorrectly decode a bitstream by referring to the flag included in theheader to switch the binarization method.

The three-dimensional decoding device may convert the decoded codingcoefficient after the quantization from an unsigned integer value to asigned integer value with a method contrary to the method performed bythe three-dimensional data encoding device. Accordingly, when the codingcoefficient is entropy encoded, the three-dimensional decoding devicecan appropriately decode a bitstream generated without considering theoccurrence of a negative integer. Note that the three-dimensionaldecoding device does not necessarily need to convert the codingcoefficient from an unsigned integer value to a signed integer value.For example, when decoding a bitstream including an encoded bit that hasbeen separately entropy encoded, the three-dimensional decoding devicemay decode the sign bit.

The three-dimensional decoding device decodes the coding coefficientafter the quantization converted to the signed integer value, by theinverse quantization and the inverse Haar conversion. Additionally, thethree-dimensional decoding device utilizes the coding coefficient afterthe decoding for the prediction after the current three-dimensionalpoint to be decoded. Specifically, the three-dimensional decoding devicecalculates the inverse quantized value by multiplying the codingcoefficient after the quantization by the decoded quantization scale.Next, the three-dimensional decoding device obtains the decoded value byapplying the inverse Haar conversion described later to the inversequantized value.

For example, the three-dimensional decoding device converts the decodedunsigned integer value to a signed integer value with the followingmethod. When the LSB (least significant bit) of the decoded unsignedinteger value a2u is 1, the signed integer value Ta1q is set to−((a2u+1)>>1). When the LSB of the decoded unsigned integer value a2u isnot 1 (when it is 0), the signed integer value Ta1q is set to (a2u>>1).

Additionally, the inverse quantized value of Ta1 is represented byTa1q×QS_L. Here, Ta1q is the quantized value of Ta1. In addition, QS_Lis the quantization step for the layer L.

Additionally, the QS may be the same value for all the layers or a partof the layers. In addition, the three-dimensional data encoding devicemay add the information indicating the QS to a header or the like.Accordingly, the three-dimensional decoding device can correctly performinverse quantization by using the same QS as the QS used by thethree-dimensional data encoding device.

Next, the inverse Haar conversion will be described. FIG. 47 is adiagram for describing the inverse Haar conversion. Thethree-dimensional decoding device decodes the attribute value of athree-dimensional point by applying the inverse Haar conversion to thecoding coefficient after the inverse quantization.

First, the three-dimensional decoding device generates the Morton codesbased on the geometry information of three-dimensional points, and sortsthe three-dimensional points in the order of the Morton codes. Forexample, the three-dimensional decoding device may perform the sortingin ascending order of the Morton codes. Note that the sorting order isnot limited to the order of the Morton codes, and the other order may beused.

Next, the three-dimensional decoding device restores the attributeinformation of three-dimensional points that are adjacent to each otherin the order of the Morton codes in the layer L, by applying the inverseHaar conversion to the coding coefficient including the low-frequencycomponent of the layer L+1, and the coding coefficient including thehigh-frequency component of the layer L. For example, thethree-dimensional decoding device may use the inverse Haar conversion ofa 2×2 matrix. The attribute information of the restored layer L is usedas the input value for the lower layer L−1.

The three-dimensional decoding device repeats such layer processing, andends the processing when all the attribute information of the bottomlayer is decoded. Note that, when only one three-dimensional pointexists as two three-dimensional points that are adjacent to each otherin the layer L−1 at the time of application of the inverse Haarconversion, the three-dimensional decoding device may assign the valueof the encoding component of the layer L to the attribute value of theone existing three-dimensional point. Accordingly, the three-dimensionaldecoding device can correctly decode a bitstream with improved codingefficiency by applying the Haar conversion to all the values of theinput attribute information.

When the attribute information is N dimensional, the three-dimensionaldecoding device may independently apply the inverse Haar conversion foreach dimension, and may decode each coding coefficient. For example,when the attribute information is color information (RGB, YUV, or thelike), the three-dimensional data decoding device applies the inverseHaar conversion to the coding coefficient for each component, anddecodes each attribute value.

The three-dimensional decoding device may apply the inverse Haarconversion in the order of Layers Lmax, L+1, . . . , L. Additionally, w0and w1 shown in FIG. 47 are the weights assigned to eachthree-dimensional point. For example, the three-dimensional datadecoding device may calculate the weight based on the distanceinformation between two adjacent three-dimensional points to which theinverse Haar conversion is applied, or the like. For example, thethree-dimensional data encoding device may decode a bitstream withimproved coding efficiency such that the closer the distance, thegreater the weight.

In the example shown in FIG. 47, the coding coefficients after theinverse quantization are Ta1, Ta5, Tb1, Tb3, Tc1, and d0, and a0, a1,a2, a3, a4, and a5 are obtained as the decoded values.

FIG. 48 is a diagram showing a syntax example of the attributeinformation (attribute_data). The attribute information (attribute_data)includes the number of consecutive zeros (ZeroCnt), the number ofattribute dimensions (attribute_dimension), and the coding coefficient(value [j] [i]).

The number of consecutive zeros (ZeroCnt) indicates the number of timesthat the value 0 continues in the coding coefficient after quantization.Note that the three-dimensional data encoding device may arithmeticallyencode ZeroCnt after binarizing ZeroCnt.

Additionally, as shown in FIG. 48, the three-dimensional data encodingdevice may determine whether or not the layer L (layerL) to which thecoding coefficient belongs is equal to or more than a predefinedthreshold value TH_layer, and may switch the information added to abitstream according to the determination result. For example, when thedetermination result is true, the three-dimensional data encoding deviceadds all the coding coefficients of the attribute information to abitstream. In addition, when the determination result is false, thethree-dimensional data encoding device may add a part of the codingcoefficients to a bitstream.

Specifically, when the determination result is true, thethree-dimensional data encoding device adds the encoded result of thethree-dimensional information of the color information RGB or YUV to abitstream. When the determination result is false, the three-dimensionaldata encoding device may add a part of information such as G or Y of thecolor information to a bitstream, and need not to add the othercomponents to the bitstream. In this manner, the three-dimensional dataencoding device can improve the coding efficiency by not adding a partof the coding coefficients of the layer (the layer smaller thanTH_layer) including the coding coefficients indicating thehigh-frequency component with less visually noticeable degradation to abitstream.

The number of attribute dimensions (attribute_dimension) indicates thenumber of dimensions of the attribute information. For example, when theattribute information is the color information (RGB, YUV, or the like)of a three-dimensional point, since the color information isthree-dimensional, the number of attribute dimensions is set to a value3. When the attribute information is the reflectance, since thereflectance is one-dimensional, the number of attribute dimensions isset to a value 1. Note that the number of attribute dimensions may beadded to the header of the attribute information of a bit stream or thelike.

The coding coefficient (value [j] [i]) indicates the coding coefficientafter quantization of the attribute information of the j-th dimension ofthe i-th three-dimensional point. For example, when the attributeinformation is color information, value [99] [1] indicates the codingcoefficient of the second dimension (for example, the G value) of the100th three-dimensional point. Additionally, when the attributeinformation is reflectance information, value [119] [0] indicates thecoding coefficient of the first dimension (for example, the reflectance)of the 120th three-dimensional point.

Note that, when the following conditions are satisfied, thethree-dimensional data encoding device may subtract the value 1 fromvalue [j] [i], and may entropy encode the obtained value. In this case,the three-dimensional data decoding device restores the codingcoefficient by adding the value 1 to value [j] [i] after entropydecoding.

The above-described conditions are (1) when attribute_dimension=1, or(2) when attribute_dimension is 1 or more, and when the values of allthe dimensions are equal. For example, when the attribute information isthe reflectance, since attribute_dimension=1, the three-dimensional dataencoding device subtracts the value 1 from the coding coefficient tocalculate value, and encodes the calculated value. The three-dimensionaldecoding device calculates the coding coefficient by adding the value 1to the value after decoding.

More specifically, for example, when the coding coefficient of thereflectance is 10, the three-dimensional data encoding device encodesthe value 9 obtained by subtracting the value 1 from the value 10 of thecoding coefficient. The three-dimensional data decoding device adds thevalue 1 to the decoded value 9 to calculate the value 10 of the codingcoefficient.

Additionally, since attribute_dimension=3 when the attribute informationis the color, for example, when the coding coefficient afterquantization of each of the components R, G, and B is the same, thethree-dimensional data encoding device subtracts the value 1 from eachcoding coefficient, and encodes the obtained value. Thethree-dimensional data decoding device adds the value 1 to the valueafter decoding. More specifically, for example, when the codingcoefficient of R, G, and B=(1, 1, 1), the three-dimensional dataencoding device encodes (0, 0, 0). The three-dimensional data decodingdevice adds 1 to each component of (0, 0, 0) to calculate (1, 1, 1).Additionally, when the coding coefficients of R, G, and B=(2, 1, 2), thethree-dimensional data encoding device encodes (2, 1, 2) as is. Thethree-dimensional data decoding device uses the decoded (2, 1, 2) as isas the coding coefficients.

In this manner, by providing ZeroCnt, since the pattern in which all thedimensions are 0 as value is not generated, the value obtained bysubtracting 1 from the value indicated by value can be encoded.Therefore, the coding efficiency can be improved.

Additionally, value [0] [i] shown in FIG. 48 indicates the codingcoefficient after quantization of the attribute information of the firstdimension of the i-th three-dimensional point. As shown in FIG. 48, whenthe layer L (layerL) to which the coding coefficient belongs is smallerthan the threshold value TH_layer, the code amount may be reduced byadding the attribute information of the first dimension to a bitstream(not adding the attribute information of the second and followingdimensions to the bitstream).

The three-dimensional data encoding device may switch the calculationmethod of the value of ZeroCnt depending on the value ofattribute_dimension. For example, when attribute_dimension=3, thethree-dimensional data encoding device may count the number of timesthat the values of the coding coefficients of all the components(dimensions) become 0. FIG. 49 is a diagram showing an example of thecoding coefficient and ZeroCnt in this case. For example, in the case ofthe color information shown in FIG. 49, the three-dimensional dataencoding device counts the number of the consecutive coding coefficientshaving 0 for all of the R, G, and B components, and adds the countednumber to a bitstream as ZeroCnt. Accordingly, it becomes unnecessary toencode ZeroCnt for each component, and the overhead can be reduced.Therefore, the coding efficiency can be improved. Note that thethree-dimensional data encoding device may calculate ZeroCnt for eachdimension even when attribute_dimension is two or more, and may add thecalculated ZeroCnt to a bitstream.

FIG. 50 is a flowchart of the three-dimensional data encoding processingaccording to the present embodiment. First, the three-dimensional dataencoding device encodes geometry information (geometry) (S6601). Forexample, the three-dimensional data encoding device performs encoding byusing an octree representation.

Next, the three-dimensional data encoding device converts the attributeinformation (S6602). For example, after the encoding of the geometryinformation, when the position of a three-dimensional point is changeddue to quantization or the like, the three-dimensional data encodingdevice reassigns the attribute information of the originalthree-dimensional point to the three-dimensional point after the change.Note that the three-dimensional data encoding device may interpolate thevalue of the attribute information according to the amount of change ofthe position to perform the reassignment. For example, thethree-dimensional data encoding device detects N three-dimensionalpoints before the change near the three dimensional position after thechange, performs the weighted averaging of the value of the attributeinformation of the N three-dimensional points based on the distance fromthe three-dimensional position after the change to each of the Nthree-dimensional points, and sets the obtained value as the value ofthe attribute information of the three-dimensional point after thechange. Additionally, when two or more three-dimensional points arechanged to the same three-dimensional position due to quantization orthe like, the three-dimensional data encoding device may assign theaverage value of the attribute information in the two or morethree-dimensional points before the change as the value of the attributeinformation after the change.

Next, the three-dimensional data encoding device encodes the attributeinformation (S6603). For example, when encoding a plurality of pieces ofattribute information, the three-dimensional data encoding device mayencode the plurality of pieces of attribute information in order. Forexample, when encoding the color and the reflectance as the attributeinformation, the three-dimensional data encoding device generates abitstream to which the encoding result of the reflectance is added afterthe encoding result of the color. Note that a plurality of encodingresults of the attribute information added to a bitstream may be in anyorder.

Additionally, the three-dimensional data encoding device may add theinformation indicating the start location of the encoded data of eachattribute information in a bitstream to a header or the like.Accordingly, since the three-dimensional data decoding device canselectively decode the attribute information that needs to be decoded,the decoding processing of the attribute information that does not needto be decoded can be omitted. Therefore, the processing amount for thethree-dimensional data decoding device can be reduced. Additionally, thethree-dimensional data encoding device may encode a plurality of piecesof attribute information in parallel, and may integrate the encodingresults into one bitstream. Accordingly, the three-dimensional dataencoding device can encode a plurality of pieces of attributeinformation at high speed.

FIG. 51 is a flowchart of the attribute information encoding processing(S6603). First, the three-dimensional data encoding device generates acoding coefficient from attribute information by the Haar conversion(S6611). Next, the three-dimensional data encoding device appliesquantization to the coding coefficient (S6612). Next, thethree-dimensional data encoding device generates encoded attributeinformation (bitstream) by encoding the coding coefficient after thequantization (S6613).

Additionally, the three-dimensional data encoding device applies inversequantization to the coding coefficient after the quantization (S6614).Next, the three-dimensional decoding device decodes the attributeinformation by applying the inverse Haar conversion to the codingcoefficient after the inverse quantization (S6615). For example, thedecoded attribute information is referred to in the following encoding.

FIG. 52 is a flowchart of the coding coefficient encoding processing(S6613). First, the three-dimensional data encoding device converts acoding coefficient from a signed integer value to an unsigned integervalue (S6621). For example, the three-dimensional data encoding deviceconverts a signed integer value to an unsigned integer value as follows.When signed integer value Ta1q is smaller than 0, the unsigned integervalue is set to −1−(2×Ta1q).

When the signed integer value Ta1q is equal to or more than 0, theunsigned integer value is set to 2×Ta1q. Note that, when the codingcoefficient does not become a negative value, the three-dimensional dataencoding device may encode the coding coefficient as the unsignedinteger value as is.

When not all coding coefficients have been processed (No in S6622), thethree-dimensional data encoding device determines whether the value ofthe coding coefficient to be processed is zero (S6623). When the valueof the coding coefficient to be processed is zero (Yes in S6623), thethree-dimensional data encoding device increments ZeroCnt by 1 (S6624),and returns to step S6622. When the value of the coding coefficient tobe processed is not zero (No in S6623), the three-dimensional dataencoding device encodes ZeroCnt, and resets ZeroCnt to zero (S6625).Additionally, the three-dimensional data encoding device arithmeticallyencodes the coding coefficient to be processed (S6626), and returns tostep S6622. For example, the three-dimensional data encoding deviceperforms binary arithmetic encoding. In addition, the three-dimensionaldata encoding device may subtract the value 1 from the codingcoefficient, and may encode the obtained value.

Additionally, the processing of steps S6623 to S6626 is repeatedlyperformed for each coding coefficient. In addition, when all the codingcoefficients have been processed (Yes in S6622), the three-dimensionaldata encoding device ends the processing.

FIG. 53 is a flowchart of the three-dimensional data decoding processingaccording to the present embodiment. First, the three-dimensionaldecoding device decodes geometry information (geometry) from a bitstream(S6631). For example, the three-dimensional data decoding deviceperforms decoding by using an octree representation.

Next, the three-dimensional decoding device decodes the attributeinformation from the bitstream (S6632). For example, when decoding aplurality of pieces of attribute information, the three-dimensionaldecoding device may decode the plurality of pieces of attributeinformation in order. For example, when decoding the color and thereflectance as the attribute information, the three-dimensional datadecoding device decodes the encoding result of the color and theencoding result of the reflectance according to the order in which theyare added to the bitstream. For example, when the encoding result of thereflectance is added after the encoding result of the color in abitstream, the three-dimensional data decoding device decodes theencoding result of the color, and thereafter decodes the encoding resultof the reflectance. Note that the three-dimensional data decoding devicemay decode the encoding results of the attribute information added to abitstream in any order.

Additionally, the three-dimensional decoding device may obtain theinformation indicating the start location of the encoded data of eachattribute information in a bitstream by decoding a header or the like.Accordingly, since the three-dimensional data decoding device canselectively decode the attribute information that needs to be decoded,the decoding processing of the attribute information that does not needto be decoded can be omitted. Therefore, the processing amount of thethree-dimensional decoding device can be reduced. Additionally, thethree-dimensional data decoding device may decode a plurality of piecesof attribute information in parallel, and may integrate the decodingresults into one three-dimensional point cloud. Accordingly, thethree-dimensional data decoding device can decode a plurality of piecesof attribute information at high speed.

FIG. 54 is a flowchart of the attribute information decoding processing(S6632). First, the three-dimensional decoding device decodes a codingcoefficient from a bitstream (S6641). Next, the three-dimensionaldecoding device applies the inverse quantization to the codingcoefficient (S6642). Next, the three-dimensional decoding device decodesthe attribute information by applying the inverse Haar conversion to thecoding coefficient after the inverse quantization (S6643).

FIG. 55 is a flowchart of the coding coefficient decoding processing(S6641). First, the three-dimensional decoding device decodes ZeroCntfrom a bitstream (S6651). When not all coding coefficients have beenprocessed (No in S6652), the three-dimensional decoding devicedetermines whether ZeroCnt is larger than 0 (S6653).

When ZeroCnt is larger than zero (Yes in S6653), the three-dimensionaldecoding device sets the coding coefficient to be processed to 0(S6654). Next, the three-dimensional decoding device subtracts 1 fromZeroCnt (S6655), and returns to step S6652.

When ZeroCnt is zero (No in S6653), the three-dimensional decodingdevice decodes the coding coefficient to be processed (S6656). Forexample, the three-dimensional decoding device uses binary arithmeticdecoding. Additionally, the three-dimensional decoding device may addthe value 1 to the decoded coding coefficient.

Next, the three-dimensional decoding device decodes ZeroCnt, sets theobtained value to ZeroCnt (S6657), and returns to step S6652.

Additionally, the processing of steps S6653 to S6657 is repeatedlyperformed for each coding coefficient. In addition, when all the codingcoefficients have been processed (Yes in S6652), the three-dimensionaldata encoding device converts a plurality of decoded coding coefficientsfrom unsigned integer values to signed integer values (S6658). Forexample, the three-dimensional data decoding device may convert thedecoded coding coefficients from unsigned integer values to signedinteger values as follows. When the LSB (least significant bit) of thedecoded unsigned integer value Ta1u is 1, the signed integer value Ta1qis set to −((Ta1u+1)>>1). When the LSB of the decoded unsigned integervalue Ta1u is not 1 (when it is 0), the signed integer value Ta1q is setto (Ta1u>>1). Note that, when the coding coefficient does not become anegative value, the three-dimensional data decoding device may use thedecoded coding coefficient as is as the signed integer value.

FIG. 56 is a block diagram of attribute information encoder 6600included in the three-dimensional data encoding device. Attributeinformation encoder 6600 includes sorter 6601, Haar transformer 6602,quantizer 6603, inverse quantizer 6604, inverse Haar converter 6605,memory 6606, and arithmetic encoder 6607.

Sorter 6601 generates the Morton codes by using the geometry informationof three-dimensional points, and sorts the plurality ofthree-dimensional points in the order of the Morton codes. Haartransformer 6602 generates the coding coefficient by applying the Haarconversion to the attribute information. Quantizer 6603 quantizes thecoding coefficient of the attribute information.

Inverse quantizer 6604 inverse quantizes the coding coefficient afterthe quantization. Inverse Haar converter 6605 applies the inverse Haarconversion to the coding coefficient. Memory 6606 stores the values ofpieces of attribute information of a plurality of decodedthree-dimensional points. For example, the attribute information of thedecoded three-dimensional points stored in memory 6606 may be utilizedfor prediction and the like of an unencoded three-dimensional point.

Arithmetic encoder 6607 calculates ZeroCnt from the coding coefficientafter the quantization, and arithmetically encodes ZeroCnt.Additionally, arithmetic encoder 6607 arithmetically encodes thenon-zero coding coefficient after the quantization. Arithmetic encoder6607 may binarize the coding coefficient before the arithmetic encoding.In addition, arithmetic encoder 6607 may generate and encode variouskinds of header information.

FIG. 57 is a block diagram of attribute information decoder 6610included in the three-dimensional decoding device. Attribute informationdecoder 6610 includes arithmetic decoder 6611, inverse quantizer 6612,inverse Haar converter 6613, and memory 6614.

Arithmetic decoder 6611 arithmetically decodes ZeroCnt and the codingcoefficient included in a bitstream. Note that arithmetic decoder 6611may decode various kinds of header information.

Inverse quantizer 6612 inverse quantizes the arithmetically decodedcoding coefficient. Inverse Haar converter 6613 applies the inverse Haarconversion to the coding coefficient after the inverse quantization.Memory 6614 stores the values of pieces of attribute information of aplurality of decoded three-dimensional points. For example, theattribute information of the decoded three-dimensional points stored inmemory 6614 may be utilized for prediction of an uncodedthree-dimensional point.

Note that, in the above-described embodiment, although the example hasbeen shown in which the three-dimensional points are encoded in theorder of the lower layers to the higher layers as the encoding order, itis not necessarily limit to this. For example, a method may be used thatscans the coding coefficients after the Haar conversion in the order ofthe higher layers to the lower layers. Note that, also in this case, thethree-dimensional data encoding device may encode the number ofconsecutive times of the value 0 as ZeroCnt.

Additionally, the three-dimensional data encoding device may switchwhether or not to use the encoding method using ZeroCnt described in thepresent embodiment per WLD, SPC, or volume. In this case, thethree-dimensional data encoding device may add the informationindicating whether or not the encoding method using ZeroCnt has beenapplied to the header information. Accordingly, the three-dimensionaldecoding device can appropriately perform decoding. As an example of theswitching method, for example, the three-dimensional data encodingdevice counts the number of times of occurrence of the codingcoefficient having a value of 0 with respect to one volume. When thecount value exceeds a predefined threshold value, the three-dimensionaldata encoding device applies the method using ZeroCnt to the nextvolume, and when the count value is equal to or less than the thresholdvalue, the three-dimensional data encoding device does not apply themethod using ZeroCnt to the next volume. Accordingly, since thethree-dimensional data encoding device can appropriately switch whetheror not to apply the encoding method using ZeroCnt according to thecharacteristic of a current three-dimensional point to be encoded, thecoding efficiency can be improved.

Hereinafter, another technique (modification) of the present embodimentwill be described. The three-dimensional data encoding device scans andencodes the coding coefficients (unsigned integer values) after thequantization according to a certain order. For example, thethree-dimensional data encoding device encodes a plurality ofthree-dimensional points from the three-dimensional points included inthe lower layers toward the higher layers in order.

FIG. 58 is a diagram showing an example of the first code sequence andthe second code sequence in the case where this technique is used forthe attribute information shown in FIG. 43. In the case of this example,the three-dimensional data encoding device encodes a plurality of codingcoefficients in the order of Ta5q, Tb1q, Tb3q, Tc1q, and d0q from Ta1qincluded in the lower layer L. Here, there is a tendency that the lowerthe layer, the more likely it is that the coding coefficient afterquantization becomes 0. This can be due to the following and the like.

Since the coding coefficients of the lower layers L show a higherfrequency component than the higher layers, the coding coefficients tendto be 0 depending on the current three-dimensional point to be encoded.Additionally, by switching the quantization scale according to theabove-described importance or the like. The lower the layer, the largerthe quantization scale, and the coding coefficient after thequantization easily become 0.

In this manner, the lower the layer, the more likely it is that thecoding coefficient after the quantization becomes 0, and the value 0 islikely to be consecutively generated for the first code sequence. Thethree-dimensional data encoding device counts the number of times thatthe value 0 occurs in the first code sequence, and encodes the number oftimes (ZeroCnt) that the value 0 consecutively occurs, instead of theconsecutive values 0. Accordingly, when there are consecutive values 0of the coding coefficients after the quantization, the coding efficiencycan be improved by encoding the number of consecutive times of 0, ratherthan encoding a lot of 0s.

Additionally, the three-dimensional data encoding device may encode theinformation indicating the total number of times of occurrence of thevalue 0. Accordingly, the overhead of encoding ZeroCnt can be reduced,and the coding efficiency can be improved.

For example, the three-dimensional data encoding device encodes thetotal number of the coding coefficients having a value of 0 asTotalZeroCnt. Accordingly, in the example shown in FIG. 58, at the timewhen the three-dimensional data decoding device decodes the secondZeroCnt (value 1) included in the second code sequence, the total numberof decoded ZeroCnts will be N+1 (=TotalZeroCnt). Therefore, thethree-dimensional data decoding device can identify that 0 does notoccur after this. Therefore, subsequently, it becomes unnecessary forthe three-dimensional data encoding device to encode ZeroCnt for eachvalue, and the code amount can be reduced.

Additionally, the three-dimensional data encoding device may entropyencode TotalZeroCnt. For example, the three-dimensional data encodingdevice binarizes the value of TotalZeroCnt with the truncated unary codeof the total number T of the encoded three-dimensional points, andarithmetically encodes each bit after binarization. At this time, thethree-dimensional data encoding device may improve the coding efficiencyby using a different coding table for each bit. For example, thethree-dimensional data encoding device uses coding table 1 for the firstbit, uses coding table 2 for the second bit, and coding table 3 for thesubsequent bits. In this manner, the three-dimensional data encodingdevice can improve the coding efficiency by switching the coding tablefor each bit.

Additionally, the three-dimensional data encoding device mayarithmetically encode TotalZeroCnt after binarizing TotalZeroCnt with anExponential-Golomb. Accordingly, when the value of TotalZeroCnt easilybecomes large, the efficiency can be more improved than the binarizedarithmetic encoding with the truncated unary code. Note that thethree-dimensional data encoding device may add a flag for switchingbetween using the truncated unary code and using the Exponential-Golombto a header. Accordingly, the three-dimensional data encoding device canimprove the coding efficiency by selecting the optimum binarizationmethod. Additionally, the three-dimensional data decoding device cancorrectly decode a bitstream by referring to the flag included in theheader to switch the binarization method.

FIG. 59 is a diagram showing a syntax example of the attributeinformation (attribute_data) in the present modification. The attributeinformation (attribute_data) shown in FIG. 59 further includes the totalnumber of zeros (TotalZeroCnt) in addition to the attribute informationshown in FIG. 48. Note that the other information is the same as that inFIG. 48. The total number of zeros (TotalZeroCnt) indicates the totalnumber of the coding coefficients having a value of 0 afterquantization.

Additionally, the three-dimensional data encoding device may switch thecalculation method of the values of TotalZeroCnt and ZeroCnt dependingon the value of attribute_dimension. For example, whenattribute_dimension=3, the three-dimensional data encoding device maycount the number of times that the values of the coding coefficients ofall the components (dimensions) become 0. FIG. 60 is a diagram showingan example of the coding coefficient, ZeroCnt, and TotalZeroCnt in thiscase. For example, in the case of the color information shown in FIG.60, the three-dimensional data encoding device counts the number of theconsecutive coding coefficients having 0 for all of the R, G, and Bcomponents, and adds the counted number to a bitstream as TotalZeroCntand ZeroCnt. Accordingly, it becomes unnecessary to encode Total ZeroCntand ZeroCnt for each component, and the overhead can be reduced.Therefore, the coding efficiency can be improved. Note that thethree-dimensional data encoding device may calculate TotalZeroCnt andZeroCnt for each dimension even when attribute_dimension is two or more,and may add the calculated TotalZeroCnt and ZeroCnt to a bitstream.

FIG. 61 is a flowchart of the coding coefficient encoding processing(S6613) in the present modification. First, the three-dimensional dataencoding device converts the coding coefficient from a signed integervalue to an unsigned integer value (S6661). Next, the three-dimensionaldata encoding device encodes TotalZeroCnt (S6662).

When not all coding coefficients have been processed (No in S6663), thethree-dimensional data encoding device determines whether the value ofthe coding coefficient to be processed is zero (S6664). When the valueof the coding coefficient to be processed is zero (Yes in S6664), thethree-dimensional data encoding device increments ZeroCnt by 1 (S6665),and returns to step S6663.

When the value of the coding coefficient to be processed is not zero (Noin S6664), the three-dimensional data encoding device determines whetherTotalZeroCnt is larger than 0 (S6666). When TotalZeroCnt is larger than0 (Yes in S6666), the three-dimensional data encoding device encodesZeroCnt, and sets TotalZeroCnt to TotalZeroCnt−ZeroCnt (S6667).

After step S6667, or when TotalZeroCnt is 0 (No in S6666), thethree-dimensional data encoding device encodes the coding coefficient,resets ZeroCnt to 0 (S6668), and returns to step S6663. For example, thethree-dimensional data encoding device performs binary arithmeticencoding. Additionally, the three-dimensional data encoding device maysubtract the value 1 from the coding coefficient, and encode theobtained value.

Additionally, the processing of steps S6664 to S6668 is repeatedlyperformed for each coding coefficient. In addition, when all the codingcoefficients have been processed (Yes in S6663), the three-dimensionaldata encoding device ends processing.

FIG. 62 is a flowchart of the coding coefficient decoding processing(S6641) in the present modification. First, the three-dimensionaldecoding device decodes TotalZeroCnt from a bitstream (S6671). Next, thethree-dimensional decoding device decodes ZeroCnt from the bitstream,and sets TotalZeroCnt to TotalZeroCnt−ZeroCnt (S6672).

When not all coding coefficients have been processed (No in S6673), thethree-dimensional data encoding device determines whether ZeroCnt islarger than 0 (S6674).

When ZeroCnt is larger than zero (Yes in S6674), the three-dimensionaldata decoding device sets the coding coefficient to be processed to 0(S6675). Next, the three-dimensional data decoding device subtracts 1from ZeroCnt (S6676), and returns to step S6673.

When ZeroCnt is zero (No in S6674), the three-dimensional data decodingdevice decodes the coding coefficient to be processed (S6677). Forexample, the three-dimensional data decoding device uses binaryarithmetic decoding. Additionally, the three-dimensional data decodingdevice may add the value 1 to the decoded coding coefficient.

Next, the three-dimensional data decoding device determines whetherTotalZeroCnt is larger than 0 (S6678). When TotalZeroCnt is larger than0 (Yes in S6678), the three-dimensional data decoding device decodesZeroCnt, sets the obtained value to ZeroCnt, sets TotalZeroCnt toTotalZeroCnt−ZeroCnt (S6679), and returns to step S6673. Additionally,when TotalZeroCnt is 0 (No in S6678), the three-dimensional decodingdevice returns to step S6673.

Additionally, the processing of steps S6674 to S6679 is repeatedlyperformed for each coding coefficient. In addition, when all the codingcoefficients have been processed (Yes in S6673), the three-dimensionaldata encoding device converts the decoded coding coefficient from anunsigned integer value to a signed integer value (S6680).

FIG. 63 is a diagram showing another syntax example of the attributeinformation (attribute_data). The attribute information (attribute_data)shown in FIG. 63 includes value [j] [i]_greater_zero_flag, value [j][i]_greater_one_flag, and value [j] [i], instead of the codingcoefficient (value [j] [i]) shown in FIG. 48. Note that the otherinformation is the same as that in FIG. 48.

Value [j] [i]_greater_zero_flag indicates whether or not the value ofthe coding coefficient (value [j] [i]) is larger than 0. In other words,value [j] hi greater_zero_flag indicates whether or not the value of thecoding coefficient (value [j] [i]) is 0.

For example, when the value of the coding coefficient is larger than 0,value [j] [i]_greater_zero_flag is set to the value 1, and when thevalue of the coding coefficient is 0, value [j] [i]_greater_zero_flag isset to the value 0. When the value of value [j] [i]_greater_zero_flag is0, the three-dimensional data encoding device need not add value [j] [i]to a bitstream. In this case, the three-dimensional decoding device maydetermine that the value of value [j] [i] is the value 0. Accordingly,the code amount can be reduced.

Value [j] [i]_greater_one_flag indicates whether or not the value of thecoding coefficient (value [j] [i]) is larger than 1 (is equal to orlarger than 2). In other words, value [j] [i]_greater_one_flag indicateswhether or not the value of the coding coefficient (value [j] RD is 1.

For example, when the value of the coding coefficient is larger than 1,value [j] [i]_greater_one_flag is set to the value 1. Otherwise (whenthe value of the coding coefficient is equal to or less than 1), value[j] [i]_greater_one_flag is set to the value 0. When the value of value[j] [i]_greater_one_flag is 0, the three-dimensional data encodingdevice need not add value [j] [i] to a bitstream. In this case, thethree-dimensional decoding device may determine that the value of value[j] [i] is the value 1.

Value [j] [i] indicates the coding coefficient after quantization of theattribute information of the j-th dimension of the i-ththree-dimensional point. For example, when the attribute information iscolor information, value [99] [1] indicates the coding coefficient ofthe second dimension (for example, the G value) of the 100ththree-dimensional point. Additionally, when the attribute information isreflectance information, value [119] [0] indicates the codingcoefficient of the first dimension (for example, the reflectance) of the120th three-dimensional point.

When value [j] [i]_greater_zero_flag=1, and value [j][i]_greater_one_flag=1, the three-dimensional data encoding device mayadd value [j] [i] to a bitstream. Additionally, the three-dimensionaldata encoding device may add the value obtained by subtracting 2 fromvalue [j] [i] to the bitstream. In this case, the three-dimensionaldecoding device calculates the coding coefficient by adding the value 2to the decoded value [j] [i].

The three-dimensional data encoding device may entropy encode value [j][i]_greater_zero_flag and value [j] [i]_greater_one_flag. For example,binary arithmetic encoding and binary arithmetic decoding may be used.Accordingly, the coding efficiency can be improved.

Embodiment 9

To achieve high compression, attribute information included in PointCloud Compression (PCC) data is transformed in a plurality of methods,such as Lifting, Region Adaptive Hierarchical Transform (RAHT) and othertransformation methods. Here, Lifting is one of transformation methodsusing Level of Detail (LoD).

Important signal information tends to be included in a low frequencycomponent, and therefore the code amount is reduced by quantizing a highfrequency component. That is, the transformation process has strongenergy compression characteristics. In addition, the precision isreduced by the quantization according to the magnitude of thequantization parameter.

FIG. 64 is a block diagram showing a configuration of athree-dimensional data encoding device according to this embodiment. Thethree-dimensional data encoding device includes subtractor 7001,transformer 7002, transformation matrix holder 7003, quantizer 7004,quantization controller 7005, and entropy encoder 7006.

Subtractor 7001 calculates a coefficient value that is the differencebetween input data and reference data. For example, the input data isattribute information included in point cloud data, and the referencedata is a predicted value of the attribute information.

Transformer 7002 performs a transformation process on the coefficientvalue. For example, the transformation process is a process ofclassifying a plurality of pieces of attribute information into LoDs.Note that the transformation process may be Haar transformation or thelike. Transformation matrix holder 7003 holds a transformation matrixused for the transformation process by transformer 7002. For example,the transformation matrix is a Haar transformation matrix. Note thatalthough an example is shown here in which the three-dimensional dataencoding device has both a function of performing a transformationprocess using LoDs and a function of performing a transformation processsuch as Haar transformation, the three-dimensional data encoding devicemay have only any one of the functions. Alternatively, thethree-dimensional data encoding device may selectively use any of thesetwo kinds of transformation processes. Alternatively, thethree-dimensional data encoding device may change the transformationprocess to be used for each predetermined processing unit.

Quantizer 7004 quantizes the coefficient value to generate a quantizedvalue. Quantization controller 7005 controls a quantization parameterused for the quantization by quantizer 7004. For example, quantizationcontroller 7005 may change the quantization parameter (or quantizationstep) according to the hierarchical structure for the encoding. In thisway, an appropriate quantization parameter can be selected for eachlayer of the hierarchical structure, so that the amount of codesoccurring in each layer can be controlled. Quantization controller 7005also sets quantization parameters for a certain layer and the layerslower than the certain layer that include a frequency component that hasa small effect on the subjective image quality at a maximum value, andsets quantization coefficients for the certain layer and the layerslower than the certain layer at 0, for example. In this way, theoccurring code amount can be reduced while reducing the deterioration ofthe subjective image quality. Quantization controller 7005 can alsofinely control the subjective image quality and the occurring codeamount. The “layer” herein refers to a layer (at a depth in a treestructure) in LoD or RAHT (Haar transformation).

Entropy encoder 7006 entropy-encodes (arithmetically encodes, forexample) the quantization coefficient to generate a bitstream. Entropyencoder 7006 also encodes the quantization parameter for each layer setby quantization controller 7005.

FIG. 65 is a block diagram showing a configuration of athree-dimensional data decoding device according to this embodiment. Thethree-dimensional data decoding device includes entropy decoder 7011,inverse quantizer 7012, quantization controller 7013, inversetransformer 7014, transformation matrix holder 7015, and adder 7016.

Entropy decoder 7011 decodes the quantization coefficient and thequantization parameter for each layer from the bitstream. Inversequantizer 7012 inverse-quantizes the quantization coefficient togenerate the coefficient value. Quantization controller 7013 controlsthe quantization parameter used for the inverse quantization by inversequantizer 7012 based on the quantization parameter for each layerobtained in entropy decoder 7011.

Inverse transformer 7014 inverse-transforms the coefficient value. Forexample, inverse transformer 7014 performs inverse Haar transformationon the coefficient value. Transformation matrix holder 7015 holds atransformation matrix used for the inverse transformation process byinverse transformer 7014. For example, the transformation matrix isinverse Haar transformation matrix.

Adder 7016 adds the reference data to the coefficient value to generateoutput data. For example, the output data is attribute informationincluded in point cloud data, and the reference data is a predictedvalue of the attribute information.

Next, the setting of a quantization parameter for each layer will bedescribed. In the encoding of attribute information, such asPredicting/Lifting, a different quantization parameter is used for eachLoD layer. For example, quantization parameters for lower layers are setto be smaller to increase the precision for the lower layers. In thisway, the prediction precision for higher layers can be improved.Quantization parameters for higher layers can be set to be greater,thereby reducing the data amount. In this way, a quantization tree value(Qt) can be separately set for each LoD, according to the use plan ofthe user. Here, the quantization tree value is the quantizationparameter, for example.

FIG. 66 is a diagram showing an example of the setting of LoDs. As shownin FIG. 66, for example, independent Qt0 to Qt2 are set for LoD0 toLoD2.

In the encoding of the attribute information using RAHT, differentquantization parameters are used according to the depth in the treestructure. For example, quantization parameters for lower layers are setto be smaller to increase the precision for the lower layers. In thisway, the prediction precision for higher layers can be improved.Quantization parameters for higher layers can be set to be greater,thereby reducing the data amount. In this way, a quantization tree value(Qt) can be separately set for each depth in the tree structure,according to the use plan of the user.

FIG. 67 is a diagram showing an example of a hierarchical structure(tree structure) of RAHT. As shown in FIG. 67, for example, independentQt0 to Qt2 are set for depths in the tree structure.

In the following, a configuration of a three-dimensional data encodingdevice according to this embodiment will be described. FIG. 68 is ablock diagram showing a configuration of three-dimensional data encodingdevice 7020 according to this embodiment. Three-dimensional dataencoding device 7020 encodes point cloud data (point cloud) to generateencoded data (encoded stream). Three-dimensional data encoding device7020 includes divider 7021, a plurality of geometry information encoders7022, a plurality of attribute information encoders 7023, additionalinformation encoder 7024, and multiplexer 7025.

Divider 7021 generates a plurality of pieces of divisional data bydividing point cloud data. Specifically, divider 7021 generates aplurality of pieces of divisional data by dividing a space of pointcloud data into a plurality of subspaces. Here, a subspace is acombination of tiles or slices or a combination of tiles and slices.More specifically, point cloud data includes geometry information,attribute information (such as color or reflectance), and additionalinformation. Divider 7021 divides geometry information into a pluralityof pieces of divisional geometry information, and divides attributeinformation into a plurality of pieces of divisional attributeinformation. Divider 7021 also generates additional informationconcerning the division.

Divider 7021 first divides a point cloud into tiles, for example.Divider 7021 then further divides the resulting tiles into slices.

The plurality of geometry information encoders 7022 generate a pluralityof pieces of encoded geometry information by encoding a plurality ofpieces of divisional geometry information. For example, geometryinformation encoders 7022 encode divisional geometry information usingan N-ary tree structure, such as an octree. Specifically, in the case ofan octree, a current space is divided into eight nodes (subspaces), and8-bit information (occupancy code) that indicates whether each nodeincludes a point cloud or not is generated. A node including a pointcloud is further divided into eight nodes, and 8-bit information thatindicates whether each of the eight nodes includes a point cloud or notis generated. This process is repeated until a predetermined layer isreached or the number of the point clouds included in each node becomesequal to or less than a threshold. For example, the plurality ofgeometry information encoders 7022 process a plurality of pieces ofdivisional geometry information in parallel.

Attribute information encoder 7023 generates encoded attributeinformation, which is encoded data, by encoding attribute informationusing configuration information generated by geometry informationencoder 7022. For example, attribute information encoder 7023 determinesa reference point (reference node) that is to be referred to in encodinga current point (current node) to be processed based on the octreestructure generated by geometry information encoder 7022. For example,attribute information encoder 7023 refers to a node whose parent node inthe octree is the same as the parent node of the current node, ofperipheral nodes or neighboring nodes. Note that the method ofdetermining a reference relationship is not limited to this method.

The process of encoding geometry information or attribute informationmay include at least one of a quantization process, a predictionprocess, and an arithmetic encoding process. In this case, “refer to”means using a reference node for calculation of a predicted value ofattribute information or using a state of a reference node (occupancyinformation that indicates whether a reference node includes a pointcloud or not, for example) for determination of a parameter of encoding.For example, the parameter of encoding is a quantization parameter inthe quantization process or a context or the like in the arithmeticencoding.

Additional information encoder 7024 generates encoded additionalinformation by encoding the additional information included in the pointcloud data and the additional information concerning the data divisiongenerated in the division by divider 7021.

Multiplexer 7025 generates encoded stream (encoded stream) bymultiplexing the plurality of pieces of encoded geometry information,the plurality of pieces of encoded attribute information, and theencoded additional information, and transmits the generated encodeddata. The encoded additional information is used in the decoding.

FIG. 69 is a block diagram of divider 7021. Divider 7021 includes tiledivider 7031 and slice divider 7032.

Tile divider 7031 generates a plurality of pieces of tile geometryinformation by dividing geometry information (position (geometry)) intotiles. Tile divider 7031 generates a plurality of pieces of tileattribute information by dividing attribute information (attribute) intotiles. Tile divider 7031 also outputs tile additional information (TileMetaData) including information concerning the tile division andinformation generated in the tile division.

Slice divider 7032 generates a plurality of pieces of divisionalgeometry information (a plurality of pieces of slice geometryinformation) by dividing a plurality of pieces of tile geometryinformation into slices. Slice divider 7032 generates a plurality ofpieces of divisional attribute information (a plurality of pieces ofslice attribute information) by dividing a plurality of pieces of tileattribute information into slices. Slice divider 7032 also outputs sliceadditional information (Slice MetaData) including information concerningthe slice division and information generated in the slice division.

Tile divider 7031 and slice divider 7032 also determine a quantizationtree value (quantization parameter) based on the generated additionalinformation.

FIG. 70 is a block diagram of attribute information encoder 7023.Attribute information encoder 7023 includes transformer 7035, quantizer7036, and entropy encoder 7037.

Transformer 7035 classifies the divisional attribute information intolayers, such as LoDs, and generates a coefficient value (differencevalue) by calculating the difference between the divisional attributeinformation and the predicted value. Note that transformer 7035 maygenerate the coefficient value by performing the Haar transformation onthe divisional attribute information.

Quantizer 7036 generates a quantized value by quantizing the coefficientvalue. Specifically, quantizer 7036 divides the coefficient by aquantization step based on the quantization parameter. Entropy encoder7037 generates encoded attribute information by entropy-encoding thequantized value.

In the following, a configuration of a three-dimensional data decodingdevice according to this embodiment will be described. FIG. 71 is ablock diagram showing a configuration of three-dimensional data decodingdevice 7040. Three-dimensional data decoding device 7040 reproducespoint cloud data by decoding encoded data (encoded stream) generated byencoding the point cloud data. Three-dimensional data decoding device7040 includes demultiplexer 7041, a plurality of geometry informationdecoders 7042, a plurality of attribute information decoders 7043,additional information decoder 7044, and combiner 7045.

Demultiplexer 7041 generates a plurality of pieces of encoded geometryinformation, a plurality of pieces of encoded attribute information, andencoded additional information by demultiplexing encoded data (encodedstream).

The plurality of geometry information decoders 7042 generates aplurality of pieces of divisional geometry information by decoding aplurality of pieces of encoded geometry information. For example, theplurality of geometry information decoders 7042 process a plurality ofpieces of encoded geometry information in parallel.

The plurality of attribute information decoders 7043 generate aplurality of pieces of divisional attribute information by decoding aplurality of pieces of encoded attribute information. For example, theplurality of attribute information decoders 7043 process a plurality ofpieces of encoded attribute information in parallel.

A plurality of additional information decoders 7044 generate additionalinformation by decoding encoded additional information.

Combiner 7045 generates geometry information by combining a plurality ofpieces of divisional geometry information using additional information.Combiner 7045 generates attribute information by combining a pluralityof pieces of divisional attribute information using additionalinformation.

FIG. 72 is a block diagram of attribute information decoder 7043.Attribute information decoder 7043 includes entropy decoder 7051,inverse quantizer 7052, and inverse transformer 7053. Entropy decoder7051 generates a quantized value by entropy-decoding encoded attributeinformation. Inverse quantizer 7052 generates a coefficient value byinverse-quantizing the quantized value. Specifically, inverse quantizer7052 multiplies the coefficient value by a quantization step based onthe quantization tree value (quantization parameter) obtained from thebitstream. Inverse transformer 7053 generates divisional attributeinformation by inverse-transforming the coefficient value. Here, theinverse transformation is a process of adding the predicted value to thecoefficient value, for example. Alternatively, the inversetransformation is the inverse Haar transformation.

In the following, an example of a method of determining a quantizationparameter will be described. FIG. 73 is a diagram showing an example ofthe setting of a quantization parameter in the tile division and theslice division.

When the value of the quantization parameter is small, the originalinformation is likely to be maintained. For example, a default value ofthe quantization parameter is 1. For example, in the encoding processusing tiles of PCC data, a quantization parameter for a tile of a mainroad is set to be a small value, in order to maintain the data quality.On the other hand, a quantization parameter for a tile of a peripheralarea is set to be a great value. In this way, the coding efficiency canbe improved, while the data quality of the peripheral area decreases.

Similarly, in the encoding process using slices of PCC data, a sidewalk,a tree, and a building are important in the self-position estimation andmapping, and a quantization parameter for a slice of a sidewalk, a tree,or a building is set to be a small value. On the other hand, a movingbody or other objects is less important, so that a quantizationparameter for a slice of a moving body or other objects is set to be agreat value.

When ΔQP (DeltaQP) described later is used, in the encoding of athree-dimensional point belonging to an important area, such as a mainroad, the three-dimensional data encoding device may perform theencoding by setting the value of ΔQP to be a negative value to reducethe quantization error, in order to decrease the quantization parameter.In this way, the decoded attribute value of the three-dimensional pointbelonging to the important area can be brought close to the value beforethe encoding. In the encoding of a three-dimensional point belonging toan area that is not important, such as a peripheral area, thethree-dimensional data encoding device may set the value of ΔQP to be apositive value to reduce the information amount, in order to increasethe quantization parameter. In this way, the total code amount can bereduced, while maintaining the amount of information on the importantarea.

In the following, an example of information that indicates aquantization parameter for each layer will be described. In encodingattribute information on a three-dimensional point by quantization, ascheme for controlling quantization parameters on a finer unit basis isintroduced in addition to quantization parameter QPbase for a frame, aslice, a tile, or the like. For example, when encoding attributeinformation using LoDs, the three-dimensional data encoding deviceperform the encoding by changing the value of the quantization parameterfor each LoD by providing Delta_Layer for each LoD and addingDelta_Layer to the value of QPbase for each LoD. The three-dimensionaldata encoding device also adds Delta_Layer used for the encoding to aheader or the like of the bitstream. In this way, the three-dimensionaldata encoding device can encode attribute information on athree-dimensional point by changing the quantization parameter for eachLoD according to a desired code amount and an actual code amount, forexample, and therefore can finally generate a bitstream having a codeamount close to the desired code amount. The three-dimensional datadecoding device can properly decode the bitstream by decoding QPbase andDelta_Layer included in the header to generate the quantizationparameters used by the three-dimensional data encoding device.

FIG. 74 is a diagram showing an example in which attribute informationon all three-dimensional points are encoded using quantization parameterQPbase. FIG. 75 is a diagram showing an example in which encoding isperformed by changing the quantization parameter for each LoD layer. Inthe example shown in FIG. 75, the quantization parameter for the leadingLoD is calculated by adding Delta_Layer of the leading LoD to QPbase.For the second and following LoDs, the quantization parameter for theLoD being processed is calculated by adding Delta_Layer of the LoD beingprocessed to the quantization parameter for the immediately precedingLoD. For example, quantization parameter QP3 of at the head of LoD3 iscalculated according to QP3=QP2+Delta_Layer[3].

Note that Delta_Layer[i] for each LoD may indicate the difference valuewith respect to QPbase. That is, quantization parameter QPi of i-th LoDiis indicated by QPi=QPbase+Delta_Layer[i]. For example,QP1=QPbase+Delta_Layer[1], and QP2=QPbase+Delta_Layer[2].

FIG. 76 is a diagram showing a syntax example of an attributeinformation header (attribute header information). Here, the attributeinformation header is a header on a frame, slice or tile basis, forexample, and is a header of attribute information. As shown in FIG. 76,the attribute information header includes QPbase (reference quantizationparameter), NumLayer (number of layers), and Delta_Layer[i](differential quantization parameter).

QPbase indicates the value of a reference quantization parameter for aframe, a slice, a tile, or the like. NumLayer indicates the number oflayers of LoD or RAHT. In other words, NumLayer indicates the number ofall Delta_Layer[i] included in the attribute information header.

Delta_Layer[i] indicates the value of ΔQP for layer i. Here, ΔQP is avalue obtained by subtracting the quantization parameter for layer ifrom the quantization parameter for layer i−1. Note that ΔQP may be avalue obtained by subtracting the quantization parameter for layer ifrom QPbase. ΔQP can assume a positive or negative value. Note thatDelta_Layer[0] need not be added to the header. In that case, thequantization parameter for layer 0 is equal to QPbase. In this way, thecode amount of the header can be reduced.

FIG. 77 is a diagram showing another syntax example of an attributeinformation header (attribute header information). The attributeinformation header shown in FIG. 77 differs from the attributeinformation header shown in FIG. 76 in that the attribute informationheader further includes delta_Layer_present_flag.

delta_Layer_present_flag is a flag that indicates whether Delta_Layer isincluded in the bitstream or not. For example, a value of 1 indicatesthat Delta_Layer is included in the bitstream, and a value of 0indicates that Delta_Layer is not included in the bitstream. Whendelta_Layer_present_flag is 0, the three-dimensional data decodingdevice performs the following decoding process by setting Delta_Layer tobe 0, for example.

Note that although examples have been described here in which thequantization parameter is indicated by QPbase and Delta_Layer, aquantization step may be indicated by QPbase and Delta_Layer. Thequantization step is calculated from the quantization parameter using aformula, a table or the like determined in advance. In the quantizationprocess, the three-dimensional data encoding device divides thecoefficient value by the quantization step. In the inverse quantizationprocess, the three-dimensional data decoding device reproduces thecoefficient value by multiplying the quantized value by the quantizationstep.

Next, an example in which the quantization parameters are controlled ona finer unit basis will be described. FIG. 78 is a diagram showing anexample in which the quantization parameters are controlled on a basisof a unit finer than LoD.

For example, when encoding attribute information using LoD, thethree-dimensional data encoding device defines ADelta_QP andNumPointADelta, which represents the geometry information on athree-dimensional point to which ADelta_QP is to be added, in additionto Delta_Layer for each LoD layer. The three-dimensional data encodingdevice performs the encoding by changing the value of the quantizationparameter based on Delta_Layer, ADelta_QP, and NumPointADelta.

The three-dimensional data encoding device may add ADelta andNumPointADelta used for the encoding to the header or the like of thebitstream. This allows the three-dimensional data encoding device toencode attribute information on a plurality of three-dimensional pointsby changing the quantization parameter for each three-dimensional pointaccording to the desired code amount and the actual code amount, forexample. In this way, the three-dimensional data encoding device canfinally generate a bitstream having a code amount close to the desiredcode amount. The three-dimensional data decoding device can properlydecode the bitstream by decoding QPbase, Delta_Layer, and ADeltaincluded in the header to generate the quantization parameters used bythe three-dimensional data encoding device.

For example, as shown in FIG. 78, quantized value QP4 of N0-th attributeinformation is calculated according to QP4=QP3+ADelta_QP[0].

An encoding/decoding order reverse to the encoding/decoding order shownin FIG. 78 can also be used. For example, encoding/decoding can also beperformed in the order of LoD3, LoD2, LoD1, and then LoD0.

FIG. 79 is a diagram showing a syntax example of an attributeinformation header (attribute header information) in the case where theexample shown in FIG. 78 is used. The attribute information header shownin FIG. 79 differs from the attribute information header shown in FIG.76 in that the attribute information header further includes NumADelta,NumPointADelta[i], and ADelta_QP[i].

NumADelta indicates the number of all ADelta_QP included in thebitstream. NumPointADelta[i] indicates an identification number ofthree-dimensional point A to which ADelta_QP[i] is applied. For example,NumPointADelta[i] indicates the number of the three-dimensional pointsfrom the leading three-dimensional point to three-dimensional point A inthe encoding/decoding order. NumPointADelta[i] may also indicates thenumber of the three-dimensional points from the first three-dimensionalpoint to three-dimensional point A in the LoD to which three-dimensionalpoint A belongs.

Alternatively, NumPointADelta[i] may indicate the difference valuebetween the identification number of the three-dimensional pointindicated by NumPointADelta[i−1] and the identification number ofthree-dimensional point A. In this way, the value of NumPointADelta[i]can be reduced, so that the code amount can be reduced.

ADelta_QP[i] indicates the value of ΔQP of the three-dimensional pointindicated by NumPointADelta[i]. That is, ADelta_QP[i] indicates thedifference between the quantization parameter of the three-dimensionalpoint indicated by NumPointADelta[i] and the quantization parameter ofthe three-dimensional point immediately preceding that three-dimensionalpoint.

FIG. 80 is a diagram showing another syntax example of an attributeinformation header (attribute header information) in the case where theexample shown in FIG. 78 is used. The attribute information header shownin FIG. 80 differs from the attribute information header shown in FIG.79 in that the attribute information header further includesdelta_Layer_present_flag and additional_delta_QP_present_flag andincludes NumADelta_minus1 instead of NumADelta.

delta_Layer_present_flag is the same as that already described withreference to FIG. 77.

additional_delta_QP_present_flag is a flag that indicates whetherADelta_QP is included in the bitstream or not. For example, a value of 1indicates that ADelta_QP is included in the bitstream, and a value of 0indicates that ADelta_QP is not included in the bitstream. Whenadditional_delta_QP_present_flag is 0, the three-dimensional datadecoding device performs the following decoding process by settingADelta_QP to be 0, for example.

NumADelta_minus1 indicates the number of all ADelta_QP included in thebitstream minus 1. In this way, by adding the value obtained bysubtracting 1 from the number of ADelta_QP to the header, the codeamount of the header can be reduced. For example, the three-dimensionaldata decoding device calculates NumADelta=NumADelta_minus1+1.ADelta_QP[i] indicates the value of i-th ADelta_QP. Note thatADelta_QP[i] can be set to be not only a positive value but also anegative value.

FIG. 81 is a flowchart of a three-dimensional data encoding processaccording to this embodiment. First, the three-dimensional data encodingdevice encodes geometry information (geometry) (S7001). For example, thethree-dimensional data encoding device performs the encoding using anoctree representation.

The three-dimensional data encoding device then transforms attributeinformation (S7002). For example, after the encoding of the geometryinformation, if the position of a three-dimensional point is changedbecause of quantization or the like, the three-dimensional data encodingdevice reassigns the attribute information on the originalthree-dimensional point to the three-dimensional point changed inposition. Note that the three-dimensional data encoding device mayperform the reassignment by interpolation of values of the attributeinformation according to the amount of change in position. For example,the three-dimensional data encoding device detects N three-dimensionalpoints yet to be changed in position close to the three-dimensionalposition of the three-dimensional point changed in position, takes aweighted average of the values of the attribute information on the Nthree-dimensional points based on the distance between thethree-dimensional positions of the three-dimensional point changed inposition and each of the N three-dimensional points, and determines theresulting value as the value of the attribute information on thethree-dimensional point changed in position. If the three-dimensionalpositions of two or more three-dimensional points are changed to thesame three-dimensional position because of quantization or the like, thethree-dimensional data encoding device may assign an average value ofthe attribute information on the two or more three-dimensional pointsyet to be changed in position as the value of the attribute informationon the three-dimensional points changed in position.

The three-dimensional data encoding device then encodes the attributeinformation (S7003). When the three-dimensional data encoding deviceencodes a plurality of pieces of attribute information, for example, thethree-dimensional data encoding device may sequentially encode theplurality of pieces of attribute information. For example, when thethree-dimensional data encoding device encodes color and reflectance asattribute information, the three-dimensional data encoding devicegenerates a bitstream including the result of encoding of color followedby the result of encoding of reflectance. Note that the plurality ofresults of encoding of attribute information can be included in thebitstream in any order.

The three-dimensional data encoding device may add informationindicating a starting point of the encoded data of each attributeinformation in the bitstream to the header or the like. In this way, thethree-dimensional data decoding device can selectively decode attributeinformation that needs to be decoded, and therefore can omit thedecoding process for attribute information that does not need to bedecoded. Therefore, the processing amount of the three-dimensional datadecoding device can be reduced. The three-dimensional data encodingdevice may encode a plurality of pieces of attribute information inparallel, and integrate the results of the encoding into one bitstream.In this way, the three-dimensional data encoding device can encode aplurality of pieces of attribute information at a high speed.

FIG. 82 is a flowchart of the attribute information encoding process(S7003). First, the three-dimensional data encoding device sets an LoD(S7011). That is, the three-dimensional data encoding device assignseach three-dimensional point to any of a plurality of LoDs.

The three-dimensional data encoding device then starts a loop on an LoDbasis (S7012). That is, the three-dimensional data encoding devicerepeatedly performs the process from step S7013 to step S7021 for eachLoD.

The three-dimensional data encoding device then starts a loop on a basisof a three-dimensional point (S7013). That is, the three-dimensionaldata encoding device repeatedly performs the process from step S7014 tostep S7020 for each three-dimensional point.

First, the three-dimensional data encoding device searches for aplurality of peripheral points, which are three-dimensional pointspresent in the periphery of the current three-dimensional point, thatare to be used for calculation of a predicted value of the currentthree-dimensional point to be processed (S7014). The three-dimensionaldata encoding device then calculates a weighted average of values of theattribute information on the plurality of peripheral points, and setsthe obtained value as predicted value P (S7015). The three-dimensionaldata encoding device then calculates a prediction residual, which is thedifference between the attribute information and the predicted value ofthe current three-dimensional point (S7016). The three-dimensional dataencoding device then calculates a quantized value by quantizing theprediction residual (S7017). The three-dimensional data encoding devicethen arithmetically encodes the quantized value (S7018). Thethree-dimensional data encoding device then determines ΔQP (S7019). ΔQPdetermined here is used for determining a quantization parameter usedfor quantization of a subsequent prediction residual.

The three-dimensional data encoding device then calculates aninverse-quantized value by inverse-quantizing the quantized value(S7020). The three-dimensional data encoding device then generates adecoded value by adding the predicted value to the inverse-quantizedvalue (S7021). The three-dimensional data encoding device then ends theloop on a basis of a three-dimensional point (S7022). Thethree-dimensional data encoding device also ends the loop on a LoD basis(S7023).

FIG. 83 is a flowchart of the ΔQP determination process (S7019). First,the three-dimensional data encoding device calculates layer i to whichcurrent three-dimensional point A to be encoded next belongs andencoding order N (S7031). Layer i indicates a LoD layer or a RAHT layer,for example.

The three-dimensional data encoding device then adds the actual codeamount to a cumulative code amount (S7032). Here, the cumulative codeamount refers to a cumulative code amount for one frame, one slice, orone tile of the current three-dimensional point. Note that thecumulative code amount may refer to a cumulative code amount for aplurality of frames, a plurality of slices, or a plurality of tiles.Alternatively, a cumulative code amount of attribute information may beused, or a cumulative code amount of both geometry information andattribute information may be used.

The three-dimensional data encoding device then determines whether thecumulative code amount is greater than the desired code amount×TH1 ornot (S7033). Here, the desired code amount refers to a desired codeamount for one frame, one slice, or one tile of the currentthree-dimensional point. Note that the desired code amount may refer toa desired code amount for a plurality of frames, a plurality of slices,or a plurality of tiles. Alternatively, a desired code amount ofattribute information may be used, or a desired code amount of bothgeometry information and attribute information may be used.

When the cumulative code amount is equal to or smaller than the desiredcode amount×TH1 (if No in S7033), the three-dimensional data encodingdevice determines whether the cumulative code amount is greater than thedesired code amount×TH2 or not (S7036).

Here, as thresholds TH1 and TH2, values from 0.0 to 1.0 are set, forexample. In addition, TH1>TH2. For example, when the cumulative codeamount is greater than the value of the desired code amount×TH1 (if Yesin S7033), the three-dimensional data encoding device determines thatthe code amount needs to be reduced as early as possible, and setsADelta_QP to value α in order to increase the quantization parameter fornext three-dimensional point N. The three-dimensional data encodingdevice also sets NumPointADelta to value N, and increment j by 1(S7034). The three-dimensional data encoding device then addsADelta_QP=α and NumPointADelta=N to the header (S7035). Note that valueα may be a fixed value or a variable value. For example, thethree-dimensional data encoding device may determine value α based onthe magnitude of the difference between the cumulative code amount andthe desired code amount×TH1. For example, the three-dimensional dataencoding device sets value α to be greater as the difference between thecumulative code amount and the desired code amount×TH1 increases. Inthis way, the three-dimensional data encoding device can control thequantization parameter so that the cumulative code amount does notexceed the desired code amount.

When the cumulative code amount is greater than the desired codeamount×TH2 (if Yes in S7036), the three-dimensional data encoding devicesets Delta_Layer to value β in order to increase the quantizationparameter for layer i to which current three-dimensional point A belongsor the subsequent layer i+1 (S7037). For example, the three-dimensionaldata encoding device sets Delta_Layer[i] of layer i to be value β whencurrent three-dimensional point A is at the top of layer i, and setsDelta_Layer[i+1] of layer i+1 to be value β when currentthree-dimensional point A is not at the top of layer i.

The three-dimensional data encoding device adds Delta_Layer=β of layer ior layer i+1 to the header (S7038). Note that value β may be a fixedvalue or a variable value. For example, the three-dimensional dataencoding device may determine value β based on the magnitude of thedifference between the cumulative code amount and the desired codeamount×TH2. For example, the three-dimensional data encoding device setsvalue β to be greater as the difference between the cumulative codeamount and the desired code amount×TH2 increases. In this way, thethree-dimensional data encoding device can control the quantizationparameter so that the cumulative code amount does not exceed the desiredcode amount.

If the cumulative code amount exceeds or is about to exceed the desiredcode amount, the three-dimensional data encoding device may set thevalue of ADelta_QP or Delta_Layer so that the quantization parameterassumes the maximum value supported by the standard or the like. In thisway, the three-dimensional data encoding device can set the quantizationcoefficient for points subsequent to three-dimensional point A or layerssubsequent to layer i to be 0, thereby reducing the increase of theactual code amount and preventing the cumulative code amount fromexceeding the desired code amount.

If the cumulative code amount is smaller than the desired codeamount×TH3, the three-dimensional data encoding device may decrease thequantization parameter so that the actual code amount increases. Forexample, the three-dimensional data encoding device may decrease thequantization parameter by setting the value of Delta_Layer or ADelta_QPto be a negative value depending on the difference between thecumulative code amount and the desired code amount. In this way, thethree-dimensional data encoding device can generate a bitstream having acode amount close to the desired code amount.

FIG. 84 is a flowchart of a three-dimensional data decoding processaccording to this embodiment. First, the three-dimensional data decodingdevice decodes geometry information (geometry) from the bitstream(S7005). For example, the three-dimensional data decoding deviceperforms the decoding using an octree representation.

The three-dimensional data decoding device then decodes attributeinformation from the bitstream (S7006). For example, when thethree-dimensional data decoding device decodes a plurality of pieces ofattribute information, the three-dimensional data decoding device maysequentially decode the plurality of pieces of attribute information.For example, when the three-dimensional data decoding device decodescolor and reflectance as attribute information, the three-dimensionaldata decoding device may decode the result of encoding of color and theresult of encoding of reflectance in the order thereof in the bitstream.For example, if the result of encoding of color is followed by theresult of encoding of reflectance in the bitstream, thethree-dimensional data decoding device first decodes the result ofencoding of color and then decodes the result of encoding ofreflectance. Note that the three-dimensional data decoding device candecode the result of encoding of attribute information in the bitstreamin any order.

The three-dimensional data decoding device may obtain the informationindicating the starting point of the encoded data of each piece ofattribute information in the bitstream by decoding the header or thelike. In this way, the three-dimensional data decoding device canselectively decode attribute information that needs to be decoded, andtherefore can omit the decoding process for attribute information thatdoes not need to be decoded. Therefore, the processing amount of thethree-dimensional data decoding device can be reduced. Thethree-dimensional data decoding device may decode a plurality of piecesof attribute information in parallel, and integrate the results of thedecoding into one three-dimensional point cloud. In this way, thethree-dimensional data decoding device can decode a plurality of piecesof attribute information at a high speed.

FIG. 85 is a flowchart of the attribute information decoding process(S7006). First, the three-dimensional data decoding device sets an LoD(S7041). That is, the three-dimensional data decoding device assignseach of a plurality of three-dimensional points having decoded geometryinformation to any of a plurality of LoDs. For example, the method ofthe assignment is the same as the method of assignment used in thethree-dimensional data encoding device.

The three-dimensional data decoding device then decodes ΔQP from thebitstream (S7042). Specifically, the three-dimensional data decodingdevice decodes Delta_Layer, ADelta_QP, and NumPointADelta from theheader of the bitstream.

The three-dimensional data decoding device then starts a loop on an LoDbasis (S7043). That is, the three-dimensional data decoding devicerepeatedly performs the process from step S7044 to step S7050 for eachLoD.

The three-dimensional data decoding device then starts a loop on a basisof a three-dimensional point (S7044). That is, the three-dimensionaldata decoding device repeatedly performs the process from step S7045 tostep S7049 for each three-dimensional point.

First, the three-dimensional data decoding device searches for aplurality of peripheral points, which are three-dimensional pointspresent in the periphery of the current three-dimensional point, thatare to be used for calculation of a predicted value of the currentthree-dimensional point to be processed (S7045). The three-dimensionaldata decoding device then calculates a weighted average of values of theattribute information on the plurality of peripheral points, and setsthe obtained value as predicted value P (S7046). Note that theseprocessings are the same as those in the three-dimensional data encodingdevice.

The three-dimensional data decoding device then arithmetically decodesthe quantized value from the bitstream (S7047). The three-dimensionaldata decoding device then calculates an inverse-quantized value byinverse-quantizing the decoded quantized value (S7048). In this inversequantization, a quantization parameter calculated using ΔQP obtained instep S7042 is used.

The three-dimensional data decoding device then generates a decodedvalue by adding the predicted value to the inverse-quantized value(S7049). The three-dimensional data decoding device then ends the loopon a basis of a three-dimensional point (S7050). The three-dimensionaldata decoding device also ends the loop on a LoD basis (S7051).

FIG. 86 is a block diagram of attribute information encoder 7023.Attribute information encoder 7023 includes LoD setter 7061, searcher7062, predictor 7063, subtractor 7064, quantizer 7065, inverse quantizer7066, reconstructor 7067, memory 7068, and ΔQP calculator 7070.

LoD setter 7061 generates a LoD using geometry information on athree-dimensional point. Searcher 7062 searches for a neighboringthree-dimensional point of each three-dimensional point using a LoDgeneration result and distance information between three-dimensionalpoints. Predictor 7063 generates a predicted value of attributeinformation of a current three-dimensional point. Predictor 7063 alsoassigns a predicted value to a plurality of prediction modes 0 to M−1,and selects a prediction mode to be used from the plurality ofprediction modes.

Subtractor 7064 generates a prediction residual by subtracting thepredicted value from the attribute information. Quantizer 7065 quantizesthe prediction residual of the attribute information. Inverse quantizer7066 inverse-quantizes the quantized prediction residual. Reconstructor7067 generates a decoded value by summing the predicted value and theinverse-quantized prediction residual. Memory 7068 stores the value(decoded value) of the decoded attribute information on eachthree-dimensional point. The decoded attribute information on thethree-dimensional points stored in memory 7068 is used for prediction ofa three-dimensional point yet to be encoded by predictor 7063.

Arithmetic encoder 7069 calculates ZeroCnt from the quantized predictionresidual, and arithmetically encodes ZeroCnt. Arithmetic encoder 7069also arithmetically encodes any quantized prediction residual that isnot zero. Arithmetic encoder 7069 may binarize the prediction residualbefore the arithmetic encoding. Arithmetic encoder 7069 may generate andencode various kinds of head information. Arithmetic encoder 7069 mayarithmetically encode prediction mode information (PredMode) thatindicates the prediction mode used for the encoding by predictor 7063,and add the information to the bitstream.

ΔQP calculator 7070 determines values of Delta_Layer, ADelta_QP, andNumPointADelta from the actual code amount obtained by arithmeticencoder 7069 and the predetermined desired code amount. The quantizationby quantizer 7065 is performed using a quantization parameter based onthe determined Delta_Layer, ADelta_QP, and NumPointADelta. Arithmeticencoder 7069 arithmetically encodes Delta_Layer, ADelta_QP, andNumPointADelta and adds these values to the bitstream.

FIG. 87 is a block diagram of attribute information decoder 7043.Attribute information decoder 7043 includes arithmetic decoder 7071, LoDsetter 7072, searcher 7073, predictor 7074, inverse quantizer 7075,reconstructor 7076, and memory 7077.

Arithmetic decoder 7071 arithmetically decodes ZeroCnt and theprediction residual included in the bitstream. Arithmetic decoder 7071also decodes various kinds of header information. Arithmetic decoder7071 also arithmetically decodes prediction mode information (PredMode)from the bitstream, and outputs the obtained prediction mode informationto predictor 7074. Arithmetic decoder 7071 also decodes Delta_Layer,ADelta_QP, and NumPointADelta from the header of the bitstream.

LoD setter 7072 generates a LoD using decoded geometry information on athree-dimensional point. Searcher 7073 searches for a neighboringthree-dimensional point of each three-dimensional point using a LoDgeneration result and distance information between three-dimensionalpoints.

Predictor 7074 generates a predicted value of attribute information of acurrent three-dimensional point to be decoded. Inverse quantizer 7075inverse-quantizes the arithmetically decoded prediction residual.Specifically, inverse quantizer 7075 performs inverse quantization usinga quantization parameter based on the decoded Delta_Layer, ADelta_QP,and NumPointADelta.

Reconstructor 7076 generates a decoded value by summing the predictedvalue and the inverse-quantized prediction residual. Memory 7077 storesthe value (decoded value) of the decoded attribute information on eachthree-dimensional point. The decoded attribute information on thethree-dimensional points stored in memory 7077 is used for prediction ofa three-dimensional point yet to be decoded by predictor 7074.

In the following, an example in which RAHT layers are used instead ofthe LoD layers will be described. FIG. 88 is a diagram showing anexample in which the quantization parameters are controlled on a basisof a finer unit when attribute information is encoded using RAHT. Forexample, when encoding attribute information using RAHT, thethree-dimensional data encoding device defines ADelta_QP andNumPointADelta, which represents the geometry information on athree-dimensional point to which ADelta_QP is to be added, in additionto Delta_Layer for each RAHT layer. The three-dimensional data encodingdevice performs the encoding by changing the value of the quantizationparameter based on Delta_Layer, ADelta_QP, and NumPointADelta.

The three-dimensional data encoding device may add ADelta andNumPointADelta used for the encoding to the header or the like of thebitstream. This allows the three-dimensional data encoding device toencode attribute information on three-dimensional points by changing thequantization parameter for each three-dimensional point according to thedesired code amount and the actual code amount, for example. In thisway, the three-dimensional data encoding device can finally generate abitstream having a code amount close to the desired code amount. Thethree-dimensional data decoding device can properly decode the bitstreamby decoding QPbase, Delta_Layer, and ADelta included in the header togenerate the quantization parameters used by the three-dimensional dataencoding device.

For example, quantized value QP4 of N0-th attribute information iscalculated according to QP4=QP3+ADelta_QP[0]. Each ADelta_QP[i] may bethe difference value with respect to QPbase, likeQP4=QPbase+ADelta_QP[0].

FIG. 89 is a diagram showing a syntax example of an attributeinformation header (attribute header information) in the case where theexample shown in FIG. 88 is used. The attribute information header shownin FIG. 89 is basically the same as the attribute information headershown in FIG. 79 but differs in that RAHT layers are used instead of LoDlayers.

NumADelta indicates the number of all ADelta_QP included in thebitstream. NumPointADelta[i] indicates an identification number ofthree-dimensional point A to which ADelta_QP[i] is applied. For example,NumPointADelta[i] indicates the number of the three-dimensional pointsfrom the leading three-dimensional point to three-dimensional point A inthe encoding/decoding order. NumPointADelta[i] may also indicates thenumber of the three-dimensional points from the first three-dimensionalpoint to three-dimensional point A in the layer to whichthree-dimensional point A belongs.

Alternatively, NumPointADelta[i] may indicate the difference valuebetween the identification number of the three-dimensional pointindicated by NumPointADelta[i−1] and the identification number ofthree-dimensional point A. In this way, the value of NumPointADelta[i]can be reduced, so that the code amount can be reduced.

FIG. 90 is a diagram showing another syntax example of an attributeinformation header (attribute header information) in the case where theexample shown in FIG. 88 is used. Note that the attribute informationheader shown in FIG. 90 is basically the same as the attributeinformation header shown in FIG. 80 but differs in that RAHT layers areused instead of LoD layers.

additional_delta_QP_present_flag is a flag that indicates whetherADelta_QP is included in the bitstream or not. For example, a value of 1indicates that ADelta_QP is included in the bitstream, and a value of 0indicates that ADelta_QP is not included in the bitstream. Whenadditional_delta_QP_present_flag is 0, the three-dimensional datadecoding device performs the following decoding process by settingADelta_QP to be 0, for example.

NumADelta_minus1 indicates the number of all ADelta_QP included in thebitstream minus 1. In this way, by adding the value obtained bysubtracting 1 from the number of ADelta_QP to the header, the codeamount of the header can be reduced. For example, the three-dimensionaldata decoding device calculates NumADelta=NumADelta_minus1+1.ADelta_QP[i] indicates the value of i-th ADelta_QP. Note thatADelta_QP[i] can be set to be not only a positive value but also anegative value.

FIG. 91 is a flowchart of a three-dimensional data encoding process inthe case where RAHT is used. First, the three-dimensional data encodingdevice encodes geometry information (geometry) (S7061). For example, thethree-dimensional data encoding device performs the encoding using anoctree representation.

The three-dimensional data encoding device then transforms attributeinformation (S7062). For example, after the encoding of the geometryinformation, if the position of a three-dimensional point is changedbecause of quantization or the like, the three-dimensional data encodingdevice reassigns the attribute information on the originalthree-dimensional point to the three-dimensional point changed inposition. Note that the three-dimensional data encoding device mayperform the reassignment by interpolation of values of the attributeinformation according to the amount of change in position. For example,the three-dimensional data encoding device detects N three-dimensionalpoints yet to be changed in position close to the three-dimensionalposition of the three-dimensional point changed in position, takes aweighted average of the values of the attribute information on the Nthree-dimensional points based on the distance between thethree-dimensional positions of the three-dimensional point changed inposition and each of the N three-dimensional points, and determines theresulting value as the value of the attribute information on thethree-dimensional point changed in position. If the three-dimensionalpositions of two or more three-dimensional points are changed to thesame three-dimensional position because of quantization or the like, thethree-dimensional data encoding device may assign an average value ofthe attribute information on the two or more three-dimensional pointsyet to be changed in position as the value of the attribute informationon the three-dimensional points changed in position.

The three-dimensional data encoding device then encodes the attributeinformation (S7063). When the three-dimensional data encoding deviceencodes a plurality of pieces of attribute information, for example, thethree-dimensional data encoding device may sequentially encode theplurality of pieces of attribute information. For example, when thethree-dimensional data encoding device encodes color and reflectance asattribute information, the three-dimensional data encoding devicegenerates a bitstream including the result of encoding of color followedby the result of encoding of reflectance. Note that the plurality ofresults of encoding of attribute information can be included in thebitstream in any order.

The three-dimensional data encoding device may add informationindicating a starting point of the encoded data of each attributeinformation in the bitstream to the header or the like. In this way, thethree-dimensional data decoding device can selectively decode attributeinformation that needs to be decoded, and therefore can omit thedecoding process for attribute information that does not need to bedecoded. Therefore, the processing amount of the three-dimensional datadecoding device can be reduced. The three-dimensional data encodingdevice may encode a plurality of pieces of attribute information inparallel, and integrate the results of the encoding into one bitstream.In this way, the three-dimensional data encoding device can encode aplurality of pieces of attribute information at a high speed.

FIG. 92 is a flowchart of the attribute information encoding process(S7063). First, the three-dimensional data encoding device generates acoding coefficient from attribute information by Haar transformation(S7071).

The three-dimensional data encoding device then applies quantization tothe coding coefficient (S7072). The three-dimensional data encodingdevice then generates encoded attribute information (bitstream) byencoding the quantized coding coefficient (S7073).

The three-dimensional data encoding device then determines ΔQP (S7074).Note that the method of determining ΔQP is the same as step S7019 in thecase where LoD layers are used. Determined ΔQP is used for determining aquantization parameter used for quantization of a subsequent codingcoefficient.

The three-dimensional data encoding device applies inverse quantizationto the quantized coding coefficient (S7075). The three-dimensional dataencoding device then decodes the attribute information by applyinginverse Haar transformation to the inverse-quantized coding coefficient(S7076). For example, the decoded attribute information is referred toin the subsequent encoding.

FIG. 93 is a flowchart of a three-dimensional data decoding process inthe case where RAHT is used. First, the three-dimensional data decodingdevice decodes geometry information (geometry) from the bitstream(S7065). For example, the three-dimensional data decoding deviceperforms the decoding using an octree representation.

The three-dimensional data decoding device then decodes attributeinformation from the bitstream (S7066). For example, when thethree-dimensional data decoding device decodes a plurality of pieces ofattribute information, the three-dimensional data decoding device maysequentially decode the plurality of pieces of attribute information.For example, when the three-dimensional data decoding device decodescolor and reflectance as attribute information, the three-dimensionaldata decoding device decodes the result of encoding of color and theresult of encoding of reflectance in the order thereof in the bitstream.For example, if the result of encoding of color is followed by theresult of encoding of reflectance in the bitstream, thethree-dimensional data decoding device first decodes the result ofencoding of color and then decodes the result of encoding ofreflectance. Note that the three-dimensional data decoding device candecode the result of encoding of attribute information in the bitstreamin any order.

The three-dimensional data decoding device may obtain the informationindicating the starting point of the encoded data of each piece ofattribute information in the bitstream by decoding the header or thelike. In this way, the three-dimensional data decoding device canselectively decode attribute information that needs to be decoded, andtherefore can omit the decoding process for attribute information thatdoes not need to be decoded. Therefore, the processing amount of thethree-dimensional data decoding device can be reduced. Thethree-dimensional data decoding device may decode a plurality of piecesof attribute information in parallel, and integrate the results of thedecoding into one three-dimensional point cloud. In this way, thethree-dimensional data decoding device can decode a plurality of piecesof attribute information at a high speed.

FIG. 94 is a flowchart of the attribute information decoding process(S7066). First, the three-dimensional data decoding device decodes thecoding coefficient from the bitstream (S7081). The three-dimensionaldata decoding device then decodes ΔQP from the bitstream (S7082).Specifically, the three-dimensional data decoding device decodesDelta_Layer, ADelta_QP, and NumPointADelta from the header of thebitstream.

The three-dimensional data decoding device then applies inversequantization to the coding coefficient (S7083). In this inversequantization, a quantization parameter calculated using ΔQP obtained instep S7082 is used. The three-dimensional data decoding device thendecodes the attribute information by applying inverse Haartransformation to the inverse-quantized coding coefficient (S7084).

FIG. 95 is a block diagram of attribute information encoder 7023 in thecase where RAHT is used. Attribute information encoder 7023 includessorter 7081, Haar transformer 7082, quantizer 7083, inverse quantizer7084, inverse Haar transformer 7085, memory 7086, arithmetic encoder7087, and ΔQP calculator 7088.

Sorter 7081 generates a Morton code using geometry information on athree-dimensional point, and sorts a plurality of three-dimensionalpoints in the order of Morton codes. Haar transformer 7082 generates acoding coefficient by applying Haar transformation to attributeinformation. Quantizer 7083 quantizes the coding coefficient of theattribute information.

Inverse quantizer 7084 inverse-quantizes the quantized codingcoefficient. Inverse Haar transformer 7085 applies inverse Haartransformation to the coding coefficient. Memory 7086 stores values ofthe decoded attribute information on the plurality of three-dimensionalpoints. For example, the decoded attribute information on thethree-dimensional points stored in memory 7086 may be used forprediction or the like of a three-dimensional point yet to be encoded.

Arithmetic encoder 7087 calculates ZeroCnt from the quantized codingcoefficient, and arithmetically encodes ZeroCnt. Arithmetic encoder 7087also arithmetically encodes any quantized coding coefficient that is notzero. Arithmetic encoder 7087 may binarize the coding coefficient beforethe arithmetic encoding. Arithmetic encoder 7087 may generate and encodevarious kinds of head information.

ΔQP calculator 7088 determines values of Delta_Layer, ADelta_QP, andNumPointADelta from the actual code amount obtained by arithmeticencoder 7087 and the predetermined desired code amount. The quantizationby quantizer 7083 is performed using a quantization parameter based onthe determined Delta_Layer, ADelta_QP, and NumPointADelta. Arithmeticencoder 7087 arithmetically encodes Delta_Layer, ADelta_QP, andNumPointADelta and adds these values to the bitstream.

FIG. 96 is a block diagram of attribute information decoder 7043 in thecase where RAHT is used. Attribute information decoder 7043 includesarithmetic decoder 7091, inverse quantizer 7092, inverse Haartransformer 7093, and memory 7094.

Arithmetic decoder 7091 arithmetically decodes ZeroCnt and the codingcoefficient included in the bitstream. Arithmetic decoder 7091 maydecode various kinds of header information. Arithmetic decoder 7091 alsodecodes Delta_Layer, ADelta_QP, and NumPointADelta from the header ofthe bitstream.

Inverse quantizer 7092 inverse-quantizes the arithmetically decodedcoding coefficient. Specifically, inverse quantizer 7092 performs theinverse quantization using a quantization parameter based on the decodedDelta_Layer, ADelta_QP, and NumPointADelta.

Inverse Haar transformer 7093 applies inverse Haar transformation to theinverse-quantized coding coefficient. Memory 7094 stores values of thedecoded attribute information on the plurality of three-dimensionalpoints. For example, the decoded attribute information on thethree-dimensional points stored in memory 7094 may be used forprediction of a three-dimensional point yet to be decoded.

In the following, a variation of this embodiment will be described. Thethree-dimensional data encoding device may encode a quantizationparameter of attribute information on each three-dimensional point asnew attribute information.

In the following, an example of a process performed by thethree-dimensional data encoding device in this case will be described.The three-dimensional data encoding device encodes attribute informationA (such as color) by calculating a quantization parameter according tothe flow shown in FIG. 82. In this process, as a new attribute value ofeach three-dimensional point, the three-dimensional data encoding deviceencodes the quantization parameter used. In this case, thethree-dimensional data encoding device may perform the encoding bychanging the value of the quantization parameter for eachthree-dimensional point. For example, if the cumulative code amount isgreater than the value of the desired code amount×TH1, thethree-dimensional data encoding device can set the value of thequantization parameter to be greater, in order to reduce the actual codeamount. If the cumulative code amount is smaller than the value of thedesired code amount×TH3, the three-dimensional data encoding device canset the value of the quantization parameter to be smaller, in order toincrease the actual code amount.

After the encoding of attribute information A, the three-dimensionaldata encoding device encodes the quantization parameter assigned to eachthree-dimensional point as new attribute information A′. In thisprocess, the three-dimensional data encoding device may apply losslessencoding to prevent losing of the amount of information on thequantization parameters. The three-dimensional data encoding device mayadd, to the header or the like, information that indicates that theencoded attribute information is a quantization parameter. In this way,the three-dimensional data decoding device can properly decode thequantization parameter used by the three-dimensional data encodingdevice.

When performing predictive encoding of attribute information using Nthree-dimensional points in the periphery of a current three-dimensionalpoint, the three-dimensional data encoding device may encode aquantization parameter on the supposition that N=1. In this way, thecalculation amount can be reduced.

Next, an example of a process performed by the three-dimensional datadecoding device will be described. First, the three-dimensional datadecoding device decodes attribute information A′ among the attributeinformation in the bitstream to obtain a quantization parameter used forthe decoding of attribute information A. The three-dimensional datadecoding device then decodes attribute information A using the decodedquantization parameter.

Note that the three-dimensional data encoding device may encode, as newattribute information A′, ΔQP, which is the amount of change of thequantization parameter between three-dimensional points, instead of thequantization parameter described above. When ΔQP assumes a positive ornegative value, the three-dimensional data encoding device may transformsigned ΔQP into a positive value before encoding ΔQP as described below.When signed ΔQP (deltaQP_s) is smaller than 0, unsigned ΔQP (deltaQP_u)is set to be −1−(2×deltaQP_s). When signed ΔQP (deltaQP_s) is equal toor greater than 0, unsigned ΔQP (deltaQP_u) is set to be 2×deltaQP_s.

The three-dimensional data encoding device may encode, as attributeinformation, a quantization parameter used for encoding of eachattribute information. For example, the three-dimensional data encodingdevice may encode a quantization parameter of attribute information A oncolor as attribute information A′, and encode a quantization parameterof attribute information B on reflectance as attribute information B′.In this way, the quantization parameter can be changed for eachattribute information. For example, if the quantization parameter ofattribute information having higher priority is set to be smaller, andthe quantization parameter of attribute information having lowerpriority is set to be greater, the total code amount can be reducedwhile preserving the attribute information having higher priority.

When quantizing and encoding a prediction residual for attributeinformation on a three-dimensional point, if delta_Layer_present_flag,additional_delta_QP_present_flag and the like indicate that Delta_Layerand ADelta_QP are set in the header, the three-dimensional data encodingdevice need not use the value of a quantization weight (QW) thatindicates the importance of a three-dimensional point. For example, whenQW is used, the quantization parameter is set to be smaller when QW isgreater (the importance is higher). In this way, it can be chosenwhether to perform the quantization based on the importance determinedby an internal process such as prediction or based on a value set in theheader by the user, so that the two manners can be selectively useddepending on the purpose of the user.

The three-dimensional data encoding device may add, to the header, aflag that indicates whether to use the value of the quantization weight(QW) or not. In this way, it can be chosen whether to perform thequantization by combining the values of Delta_Layer and ADelta_QP and QWor not, the two manners can be selectively used depending on the purposeof the user.

When quantizing and encoding a transformation coefficient for attributeinformation on a three-dimensional point using RAHT or the like, ifdelta_Layer_present_flag, additional_delta_QP_present_flag and the likeindicate that Delta_Layer and ADelta_QP are set in the header, thethree-dimensional data encoding device need not use the value of thequantization weight (QW). In this way, it can be chosen whether toperform the quantization based on the importance determined by aninternal process such as prediction or based on a value set in theheader by the user, so that the two manners can be selectively useddepending on the purpose of the user. Furthermore, the three-dimensionaldata encoding device may add, to the header, a flag that indicateswhether to use the value of quantization weight (QW) or not. In thisway, it can be chosen whether to perform the quantization by combiningthe values of Delta_Layer and ADelta_QP and QW or not, the two mannerscan be selectively used depending on the purpose of the user.

FIG. 97 is a diagram showing a syntax example of an attributeinformation header (attribute header information) in this case. Theattribute information header shown in FIG. 97 differs from the attributeinformation header shown in FIG. 80 in that the attribute informationheader further includes default_delta_Layer_present_flag,default_delta_Layer_index, default_additional_delta_QP_present_flag, anddefault_additional_delta_QP_index.

default_delta_Layer_present_flag is a flag that indicates whether to usean initially set value of Delta_Layer defined by a standard or the likeor not. For example, a value of 1 indicates that initially setDelta_Layer is to be used. A value of 0 indicates that initially setDelta_Layer is not to be used. In the case of the value of 0, thethree-dimensional data decoding device performs the following decodingprocess by setting Delta_Layer to be 0, for example.

default_delta_Layer_index is information that allows identification ofDelta_Layer to be used among one or more initially set values ofDelta_Layer defined by a standard or the like. For example,default_delta_Layer_index is defined as described below.

When default_delta_Layer_index=0, Delta_Layer for all layers is set tobe 1. That is, the value of the quantization parameter increases by 1every time a layer is incremented. When default_delta_Layer_index=1,Delta_Layer for all layers is set to be 2. That is, the value of thequantization parameter increases by 2 every time a layer is incremented.

If an initially set Delta_Layer is defined by a standard or the like inthis way, the quantization parameter can be changed without adding thevalue of Delta_Layer to the header, so that the code amount of theheader can be reduced.

default_additional_delta_QP_present_flag is a flag that indicateswhether to use an initially set value of ADelta_QP defined by a standardor the like or not. For example, a value of 1 indicates that initiallyset ADelta_QP is to be used. A value of 0 indicates that initially setADelta_QP is not to be used. In the case of the value of 0, thethree-dimensional data decoding device performs the following decodingprocess by setting ADelta_QP to be 0, for example.

default_additional_delta_QP_index is information that allowsidentification of ADelta_QP to be used among one or more values ofinitially set ADelta_QP defined by a standard or the like. For example,default_additional_delta_QP_index is defined as described below.

When default_additional_delta_QP_index=0, ADelta_QP is set to be 1 everyN three-dimensional points. That is, the value of the quantizationparameter increases by 1 each time N three-dimensional points areencoded or decoded. Note that the three-dimensional data encoding devicemay additionally add information indicating N to the header.

When default_additional_delta_QP_index=1, ADelta_QP is set to be 2 everyN three-dimensional points. That is, the value of the quantizationparameter increases by 2 each time N three-dimensional points areencoded or decoded. Note that the three-dimensional data encoding devicemay additionally add information indicating N to the header.

If an initially set ADelta_QP is defined by a standard or the like inthis way, the quantization parameter can be changed without adding thevalue of ADelta_QP to the header, so that the code amount of the headercan be reduced.

Embodiment 10

In the process of assigning a quantization parameter to each layerdescribed in Embodiment 9, a process according to Embodiment 10 can alsobe performed.

In this embodiment, an example where a QP value applied in quantizationis assigned on a layer basis will be described. FIG. 98 is a graphshowing a relationship between bitrate of encoding of a bitstream andtime.

As shown in FIG. 98, the three-dimensional data encoding devicedesirably controls the bitrate of encoding within a predetermined rangebetween threshold TH1 and threshold TH2 that is permitted in advance.Threshold TH1 is a maximum threshold (upper limit value) of thepredetermined range. Specifically, threshold TH1 is a hard limit thatcannot be exceeded because of the limitation of the buffer ortransmission bandwidth. Threshold TH2 is a minimum threshold (lowerlimit value) of the predetermined range. Specifically, threshold TH2 isa soft limit that is set to maintain the consistency of the bitrate andthe image quality.

In order to control the bitrate of encoding within the range betweenthreshold TH1 and threshold TH2, adjustment of the QP value may berequired during encoding. The process of adjusting the QP value so thatthe bitrate falls within a predetermined range can be easily implementedusing an adjustment tool for the QP value. In the process of adjustingthe QP value, the bitrate is increased and decreased by adjusting the QPvalue in accordance with the number of three-dimensional points havingan attribute value with which the QP value is to be associated and thecomplexity of the attribute value to be encoded.

Variations of the bitrate occur in an early stage of the adjustment ofthe QP value to a proper value by an encoding tool. Once the encodedstream becomes stable, the variations of the bitrate become small, andthe bitrate eventually becomes stable.

FIG. 99 is a diagram showing a hierarchical structure of athree-dimensional point cloud and the number of three-dimensional pointsbelonging to each layer.

As shown in part (a) of FIG. 99, the plurality of three-dimensionalpoints included in the three-dimensional point cloud are classified intofour layers, layer 1, layer 2, layer 3, and layer 4.

As shown in FIG. 99, the number of three-dimensional points may besignificantly different between different LoD layers or different depthlayers, depending on the properties of the predictive transformation,the lifting transformation, or the RAHT. For example, layer 4, which isthe bottom layer, includes 75% of the three-dimensional points in thethree-dimensional point cloud, and the other layers 1 to 3 include theremaining 25% of the three-dimensional points. Therefore, changing theQP value for layer 4 has a greater effect on the size or bitrate of thebitstream than changing the QP value for any of layers 1 to 3.

This method can be applied not only to the predictive transformation,the lifting transformation, and the RAHT but also to another method thatuses a plurality of layers for encoding a three-dimensional point cloud.That is, this method is not limited to the application to thethree-dimensional points classified into layers illustrated above, asfar as an original QP value (for a slice or layer) to be applied can beapplied to a group of pieces of three-dimensional point cloud data.Furthermore, when one layer is further divided into a plurality ofsub-layers (SubLayer), and a plurality of three-dimensional pointsincluded in the one layer are assigned to (classified into) any of theresulting plurality of sub-layers, ΔQP (DeltaQP) may be assigned to eachof the plurality of sub-layers. Here, one sub-layer has only to beassigned one or more three-dimensional points.

The process of further dividing one layer into a plurality of sub-layersis not necessarily applied to the bottom layer. The process can beapplied to any of the plurality of layers.

In order to effectively control the bitrate of the bitstream in theencoding, an adjustment tool for more finely adjusting the QP value isneeded, in addition to performing the quantization by adjusting the QPvalue for each layer. Thus, in a method using NumPointADelta, the usercan set the quantity of an attribute value for a particular layer of aparticular slice to be a desired value.

FIG. 100 is a diagram showing a first example of the classification of athree-dimensional point cloud in one layer into sub-layers eachincluding a specified number of three-dimensional points. In the firstexample, the number of the three-dimensional points included in each ofthe plurality of sub-layers is specified.

In the division into sub-layers, the three-dimensional point cloud canbe divided in many methods, depending on the situation or the encodingmethod. For example, it is possible that, by directly specifying theamount of the three-dimensional point cloud in each sub-layer, a layeris divided into a plurality of sub-layers in accordance with thespecified amounts of three-dimensional point clouds.

For example, in FIG. 100, layer 4 includes 100 three-dimensional pointsto be encoded, and layer 4 is divided into two sub-layers each including25 three-dimensional points and one sub-layer including 50three-dimensional points. For example, the number of the sub-layersobtained by dividing layer 4 and the number of the three-dimensionalpoints included in each sub-layer may be specified by user input.

In the syntax example of the header shown in FIG. 80, NumPointADelta[i]is used for storing three-dimensional point information on a sub-layer.In this example, the array size is 3, or NumPointADelta[i] is set to bea value from 0 to 2 that indicate three sub-layers of layer 4. irepresents a value that indicates the layer that includes thesub-layers. NumPointADelta[i] may indicates the size of each sub-layeror indicates the starting point of each sub-layer. The size of asub-layer is the number of the three-dimensional points included in thesub-layer. The starting point of a sub-layer is the smallest serialnumber of the serial numbers of the plurality of three-dimensionalpoints included in the sub-layer (the serial number of the leadingthree-dimensional point in the sub-layer) when a serial number isassigned to each three-dimensional point in the three-dimensional pointcloud included in the layer to which the sublayer belongs.

When NumPointADelta[i] indicates size, the three sub-layers included inlayer 4 in FIG. 100 can be expressed as NumPointADelta[3]=[25, 25, 50],for example. In this case, the encoding size, that is, the encodingcoverage, is smaller in most cases, so that a slightly smaller parametervalue can be generated. However, both the three-dimensional dataencoding device and the three-dimensional data decoding device have tokeep track of the size of the sub-layer being processed.

When NumPointADelta[i] indicates starting point, the three sub-layersincluded in layer 4 in FIG. 100 can be expressed asNumPointADelta[3]=[0, 25, 50], for example. In this case, as theencoding position, the overall range of the last element is alwaysrequired. On the other hand, the counter for the three-dimensional pointcloud is constantly kept track of, so that the three-dimensional dataencoding device and the three-dimensional data decoding device can moreeasily perform the processing.

FIG. 101 is a diagram showing a second example of the classification ofa three-dimensional point cloud in one layer into sub-layers eachincluding the same number of three-dimensional points. FIG. 102 shows asyntax example of a header of attribute information in the secondexample. FIG. 103 shows another syntax example of attribute informationin the second example. In the second example, a common number ofthree-dimensional points is specified for a plurality of sub-layers.That is, in the second example, one common number is specified as thenumber of the three-dimensional points classified into each sub-layer.

In the second example, as shown in FIG. 102, const_NumPoint may beencoded, and ADelta_QP may be encoded for each sub-layer. As shown inFIG. 103, const_NumPoint may be generated for each sub-layer and changedfor each layer.

additional_delta_QP_present_flag is implemented to indicateconst_NumPoint, and ADelta_QP can be used for each sub-layer. Whenconst_NumPoint need to be constantly encoded,additional_delta_QP_present_flag may be omitted. Similarly, TotalPointfor each sub-layer may be internally calculated in the encoding ordecoding processing, or may be encoded and stored in the header forsimplification.

const_NumPoint indicates the number (constant) of the three-dimensionalpoints in each sub-layer.

num_sublayer indicates the number of sub-layers that can be generated bydivision based on the total number of the three-dimensional points inthe sub-layers and const_NumPoint.

By setting the number of the three-dimensional points included in eachsub-layer to be a constant in this way, the overhead of the encoding ordecoding can be reduced.

FIG. 104 is a diagram showing a third example of the classification of athree-dimensional point cloud in one layer into a different number ofsub-layers than predetermined. FIG. 105 shows a syntax example of aheader of attribute information in the third example. FIG. 106 showsanother syntax example of a header of attribute information in the thirdexample. In the third example, a plurality of three-dimensional pointsare classified into a different number of sub-layers than predetermined,such as a larger number of sub-layers than a predetermined number.

In this example, since a larger number of sub-layers than predeterminedare generated, a sub-layer that is not assigned a DeltaQP value occurs.In this case, a default value or a predetermined value, such as 0, maybe set as a DeltaQP value for a sub-layer that is not assigned a DeltaQPvalue. Alternatively, the DeltaQP value assigned to one (such as thelast or bottom sub-layer) of the sub-layers assigned a DeltaQP value maybe assigned to the sub-layer that is not assigned a DeltaQP value as aDeltaQP value for the sub-layer that is not assigned a DeltaQP value. Inthat case, the number of ADeltaQP values required for the encoding canbe reduced, so that the overhead can be reduced.

Note that the last sub-layer (layer) is the (n−1)-th layer of n layersfrom layer 0 to layer n−1 determined in a predetermined method by thethree-dimensional data encoding device. The number of layers is added tothe header. For example, the predetermined method is a method in whichwhen the three-dimensional data encoding device determines that adesired bitrate will be achieved and the QP values for the subsequentsub-layers need not be changed in the course of encoding of the layersby performing rate control, the three-dimensional data encoding devicedoes not transmit the DeltaQP values for the subsequent layers. This canreduce the code amount of the header.

There are a plurality of examples of the syntax that identifies thenumber of ADeltaQP values to be encoded or decoded. For example, FIG.105 shows a syntax that includes a particular stop_code, which is afixed value for a range of DeltaQP for a sub-layer. Thethree-dimensional data decoding device ends the loop when thethree-dimensional data decoding device obtains stop_code. The range ofDeltaQP need to be encoded by the three-dimensional data encoding deviceor may be defined by a standard so that both the three-dimensional dataencoding device and the three-dimensional data decoding device canrecognize the range. As another example, FIG. 106 shows a syntax inwhich num_sublayer is directly encoded to indicate the number ofADeltaQP values that can be decoded.

Note that, when a smaller number of sub-layers than predetermined aregenerated, and there is an excessive number of ADeltaQP values when thenumber of sub-layers is referred to, the excessive number of ADeltaQPvalues need not be used and can be discarded.

FIG. 107 is a diagram showing a fourth example of the classification ofa three-dimensional point cloud in one layer into sub-layers eachincluding a specified ratio (percentage) of the three-dimensionalpoints. FIG. 108 shows an example of a syntax of a header of attributeinformation in the fourth example. In the fourth example, the ratio usedfor the classification into sub-layers are specified as the number ofthree-dimensional points to be classified into each sub-layer.

In this example, the number of the three-dimensional points in asub-layer is specified by the ratio of the number of thethree-dimensional points in the sub-layer to the number of thethree-dimensional points included in the layer including the sub-layer.FIG. 107 shows that four sub-layers include 25%, 30%, 30%, and 15% ofthe total number of the three-dimensional points included in the layerincluding the respective sub-layers. When a layer is divided into aplurality of sub-layers in this way, the number of the three-dimensionalpoints included in each of the plurality of sub-layers generated bydivision may be indicated by the ratio of the number ofthree-dimensional points included in the sub-layer to the total numberof the three-dimensional points included in the layer. In this case,both the three-dimensional data encoding device and thethree-dimensional data decoding device keep track of the number ofthree-dimensional points to be encoded for the respective processings.The ratio may be calculated with respect to the whole of thethree-dimensional point cloud or may be calculated as a ratio for aparticular layer, depending on the method to be implemented.

In FIG. 108, num_sublayer indicates the number of the sub-layers dividedby the ratio of the number of the three-dimensional points. percentileindicates the ratio of the total number of the three-dimensional pointsincluded in a relevant sub-layer to the total number of thethree-dimensional points included in the layer to which the sub-layerbelongs.

For example, when the sum of the ratios is less than 100%, such as whena plurality of three-dimensional points remain without being classifiedinto any sub-layer, the plurality of remaining three-dimensional pointsmay be classified into an additional sub-layer or into the previoussub-layer, depending on the implementation shared by both thethree-dimensional data encoding device and the three-dimensional datadecoding device. On the other hand, when an error occurs, and the sum ofthe ratios is greater than 100%, which is an allowable value, thedivision into sub-layers ends when the last three-dimensional point isreached.

Note that, since the sum of the ratios is always fixed at 100%, the lastratio of the plurality of ratios specified for classifying a pluralityof three-dimensional points included in one layer into a plurality ofsub-layers can be omitted. This means that the number of the ratioelements is smaller than the number of the ADelta_QP elements by 1.

FIG. 109 is a diagram showing a fifth example of the classification of athree-dimensional point cloud in one layer into sub-layers based onMorton indices. FIG. 110 is a syntax example of a header of attributeinformation in the fifth example.

In the fifth example, Morton indices in the Morton code for athree-dimensional point cloud are specified for a plurality ofsub-layers. Specifically, in the fifth example, a three-dimensionalpoint cloud included in one layer is classified into sub-layers usingthe Morton code. For example, a plurality of three-dimensional pointshaving a common Morton index may be classified into the same sub-layer.In that case, in the three-dimensional point cloud, three-dimensionalpoints spatially close to each other, such as three-dimensional pointswithin a predetermined distance, are grouped. Therefore, for example,the three-dimensional point cloud included in each sub-layer is includedin one three-dimensional space, and the three-dimensional spacecorresponding to one of a plurality of sub-layers does not overlap withthe three-dimensional spaces corresponding to the other sub-layers. Inthis way, one sub-layer is assigned a three-dimensional point cloudincluding three-dimensional points spatially close to each other, andthe three-dimensional points in the three-dimensional point cloud arelikely to have similar characteristics or attributes. Therefore,three-dimensional points included in the same sub-layer are encodedusing a common QP value, so that the encoding efficiency can beimproved.

When a three-dimensional point cloud is arranged in Morton order in eachlayer, sorting of the three-dimensional points need not be performed.The three-dimensional data encoding device can determine, in advance,which three-dimensional points are to be classified into the samesub-layer, and determine the number of the three-dimensional points tobe classified into each sub-layer in the method described above.

num_morton_sublayer indicates the number of sub-layers generated bydivision in the grouping using Morton order. Index indicates a Mortonindex. For example, when Index indicates that Morton index A in FIG. 109is selected, child nodes of a node of Morton index A are set to besub-layers. A starting point or ending point of a correspondingsub-layer may be indicated using the Morton code. The Morton index isnot exclusively obtained from a three-dimensional point in the bottomlayer but can also be obtained from a three-dimensional point in the toplayer. A starting point or ending point for classifyingthree-dimensional points into each sub-layer may be determined dependingon the settings of both the three-dimensional data encoding device andthe three-dimensional data decoding device.

FIG. 111 and FIG. 112 are diagrams showing a sixth example of theclassification of a three-dimensional point cloud in one layer intosub-layers based on Morton indices. In the sixth example, athree-dimensional point cloud is classified using a RAHT hierarchicalstructure.

In the sixth example, sub-layers A to C, which are groups of classifiedthree-dimensional points, are identified using Morton indices forthree-dimensional points. In this way, three-dimensional points having acommon Morton index are classified into the same sub-layer, as in thefifth example.

A three-dimensional point cloud can be classified based on Mortonindices of the three-dimensional point cloud, depending on thethree-dimensional data encoding device and the three-dimensional datadecoding device. A setting of sub-layers can be applied to any layer. Athree-dimensional point located higher than a specified Morton index maybe classified into the subsequent sub-layer or may be handled as anoutlier.

FIG. 112 shows the last three-dimensional points in sub-layersclassified based on Morton indices. In FIG. 112, sub-layers similar tothose in FIG. 111 are shown in a one-dimensional arrangementrepresentation.

The classification into sub-layers using Morton indices is notexclusively applied to the classification using a specific layerstructure such as depths of RAHT, but can also be applied to theclassification at a plurality of layers or depths. In that case,sub-layers are sub-groups.

FIG. 113 is a diagram showing a seventh example of the classification ofa three-dimensional point cloud in one layer into sub-layers using aresidual or Delta value.

In the seventh example, for example, a Delta value is attributeinformation (attribute value) by which a quantization weight functionfor LoD layers in the predictive transformation or the lifting is to bemultiplied. Similarly, in the RAHT, a Delta value is attributeinformation (attribute value) by which a weight is to be multiplied. TheDelta value may be a value to be encoded as attribute information, andmay be a value after prediction depending on the encoding processing,regardless of which of the predictive transformation, the lifting, theRAHT, and any other method is used for transformation. Note that, inFIG. 113, “Delta” denotes a Delta value.

When a Delta value is small, the Delta value can be encoded with aslightly small QP so that details are maintained and not quantized. Inthis way, a reduction of the resolution can be prevented. However, whena Delta value is large a large QP value can be used, since a largedifference in Delta value is not easily quantized.

In FIG. 113, the radius of a circle represents the magnitude of Deltavalues. The larger the Delta value, the farther from the center thethree-dimensional point is.

FIG. 114 is a diagram showing an arrangement of three-dimensional pointsarranged in a two-dimensional Morton order. FIG. 115 shows a syntaxexample of a header of attribute information in the seventh example.

When Delta values are used to classify a three-dimensional point cloudinto a plurality of sub-layers like in the seventh example, all thethree-dimensional points covered by a QP value for a sub-layer are notconsecutive. For example, as shown in FIG. 114, some three-dimensionalpoints are classified into group A, other discrete three-dimensionalpoints are classified into group B, and some of the remainingthree-dimensional points are classified into group C.

A three-dimensional point cloud is assigned to a group index value andthe index value is encoded along with a Delta value. Note that encodingfor lower levels will be described later. Note that the index value maybe encoded as additional attribute information or as a SEI message.

In the seventh example, an additional index needs to be encoded for eachthree-dimensional point cloud, so that encoding using DeltaQP is madepossible for a sub-layer of a cross layer, although an overhead canoccur. Note that the three-dimensional data encoding device mayrearrange three-dimensional points in a particular layer afterdetermining indices, and then encode the indices and corresponding QPindices in ascending or descending order.

num_Group indicates the number of groups or sub-layers generated bydivision based on Delta values.

FIG. 116 shows a syntax example of a bitstream of a residual. FIG. 117shows a formula for calculating an encoding cost (Encoding cost). FIG.118 is a graph showing a relationship between bits per point (BPP) andtime.

As shown in FIG. 116, when a bitstream of a residual is being encoded,index information is encoded for each point cloud. Note that indexindicates an index number of a sub-layer or sub-group to which athree-dimensional point belongs. values indicates a prediction residual(residual value).

Note that, in determining an appropriate index for eachthree-dimensional point, the encoding cost (see FIG. 117) for each pieceof attribute information may be used, or the encoding cost of apreviously encoded three-dimensional point may be used. The encodingcost in this context is referred to as a bit count required for encodingattribute information on each three-dimensional point cloud. Theencoding cost is an objective value used for approximately determiningan index according to a formula or for accurately determining an indexbased on a previously encoded three-dimensional point.

As shown in FIG. 118, the three-dimensional data encoding device maycontrol BPP by classifying (grouping) a plurality of three-dimensionalpoints using an index based on the bits per point frequency (BPP rate)or, in other words, based on the cost of the number of three-dimensionalpoints encoded or decoded per second and applying different sub-layerQPs based on the encoding cost. This method is suitable for controllingthe bitrate of PCC encoding.

This method operates in the same manner in a frame-based encoding of athree-dimensional point cloud, and therefore, the bitrate or bufferstatus is useful in the classification into sub-layers. When thethreshold of the buffer is being approached, the classification(grouping) is preferably performed using a high DeltaQP value inaddition to the encoding cost of the three-dimensional points.

In the seventh example, as in all the examples described above, thenumber of sub-layers (groups) needs to be defined by a correspondingDeltaQP value. For example, in the syntax shown in FIG. 115, the numberof sub-layers is defined in the header of attribute information or SPS.

FIG. 119 is a diagram showing that a QP value applied to the encoding ofattribute information is set for each sub-layer. Part (a) of FIG. 119shows a case where the method in which a QP value is set for eachsub-layer is applied to a RAHT hierarchical structure, and part (b)shows a case where the method is applied to a LoD hierarchicalstructure.

The method that uses an index described in the seventh example can beused in combination of a PCC RDOQ (Rate-distortion Optimizedquantization) method, and is based on an optimal combination ofdistortion and bit cost.

RDOQ can be recursively implemented for attribute information on eachthree-dimensional point cloud by using various settings of DeltaQP forsublayers. To reduce the processing time, DeltaQP set for each sub-layercan be set first (in advance) in the header of attribute information orSPS. When set in SPS, DeltaQP is common to all the sub-layers. In RDOQ,only by recursively setting these values of DeltaQP, the encoding isefficiently achieved using an index for a particular sub-layer in thesyntax of the bitstream of the residual.

FIG. 120 is a diagram showing an eighth example of the classification ofa three-dimensional point cloud into sub-layers using the Morton code.FIG. 121 is a syntax example of a header of attribute information in theeighth example.

In this embodiment, although a method that groups a three-dimensionalpoint cloud into a plurality of sub-layers (groups) based on Mortoncodes for three-dimensional points and sets ADelta_QP for each sub-layer(each group) has been described in the fifth example and the sixthexample, the present disclosure is not necessarily limited thereto. Forexample, a three-dimensional point cloud may be grouped based ongeometry information (x, y, z) on three-dimensional points, andADelta_QP may be set for each group.

Specifically, center coordinate cA, radius rA, and ADelta_QP_A aredefined for group A, and center coordinate cB, radius rB, andADelta_QP_B are defined for group B. When there are other groups,similarly, the center coordinate, the radius, and ADelta_QP are definedfor each group. The center coordinate, the radius, and ADelta_QP definedfor each group, the total number of groups and the like are added to theheader of the attribute information.

The three-dimensional data encoding device can apply ADelta_QP_A when aposition coordinate of the three-dimensional point to be processed isincluded in group A (a sphere having center coordinate cA and radiusrA), and apply ADelta_QP_B when a position coordinate of thethree-dimensional point to be processed in included in group B (a spherehaving center coordinate cB and radius rB). When the three-dimensionalpoint to be processed is included in both the spheres of group A andgroup B, the three-dimensional data encoding device may calculate thedistances between the three-dimensional point to be processed and centercoordinate cA and cB of the groups and apply ADelta_QP of the group withthe shorter distance.

In this way, close values of ADelta_QP can be applied tothree-dimensional points close to each other in the three-dimensionalspace, the subjective image quality of encoded or decodedthree-dimensional points can be controlled on a region basis.

When it is desired to improve the subjective image quality of an object,the three-dimensional data encoding device may designate the centercoordinate of a three-dimensional point forming the object as the centercoordinate of group 101, designate a half of the size of the object asthe radius of group 101, and set ADelta_QP of group 101 to be a negativevalue, for example. In this way, the quantization step value of theencoding of the attribute of a three-dimensional point belonging togroup 101 (included in the sphere of group 101) can be reduced, and as aresult, the subjective image quality of the object can be improved.

Note that FIG. 120 shows groups 101 and 102 in a three-dimensional spaceinto which a three-dimensional point cloud is classified. Group 101includes three-dimensional point 121, which is included in a sphericalspace having central point O1 and radius R1. Group 102 includesthree-dimensional point 122, which is included in a spherical spacehaving central point O2 and radius R2. To the encoding of thethree-dimensional points belonging to group 101, ADelta_QP_O set forgroup 101 is applied. To the encoding of the three-dimensional pointsbelonging to group 102, ADelta_QP_P set for group 102 is applied.Central point O1 and central point O2, which are reference points fordefining the respective groups, are expressed using coordinate values onthree axes.

There may be three-dimensional point 123 included in both group 101 andgroup 102. To the encoding of three-dimensional point 123, the sum oraverage of ADelta_QP_O set for group 101 and ADelta_QP_P set for group102 can be applied. In this way, QP values can be more finelycontrolled. Each three-dimensional point may belong to a plurality ofgroups. In that case, the sum of ADelta_QP of all the groups to whichthe three-dimensional point to be processed belongs can be used as thevalue of ADelta_QP of the three-dimensional point to be processed. Inthis way, QP values for a region in the three-dimensional space can bemore flexibly controlled.

Note that ADelta_QP_O for group 101 may be set to be a negative value todecease the QP value, for example. In this way, a deterioration of theattribute information on the three-dimensional points belonging to group101 due to the encoding can be prevented.

To the contrary, ADelta_QP_O for group 0 may be set to be a positivevalue to increase the QP value, for example. In this way, the codeamount of the attribute information on the three-dimensional pointsbelonging to group 101 can be reduced.

Note that although an example has been shown in which a sphere is usedas a three-dimensional space for defining a group, the presentdisclosure is not necessarily limited thereto, and a group can also bedefined using an ellipsoid or a cube. In that case, a parameter thatdefines the shape may be added to the header or control information.

Note that, in FIG. 121, num_group indicates the total number of groups.center_x, center_y, and center_z indicates a center coordinate of eachgroup.

radius indicates the radius of each group. When the group used is anellipsoid or a cube, a parameter that indicates the shape may be addedto the header or the like. For example, when a cube or a rectangularparallelepiped is used, a parameter that indicates coordinates of areference point, and a width, a depth, and a height from the referencepoint may be added to the header or the like. When a plurality ofshapes, such as a sphere and an ellipsoid, are used as shapes ofthree-dimensional spaces for defining groups, information that indicatesthe shape of a relevant three-dimensional space may be added to theheader.

As stated above, a three-dimensional data encoding device according toone aspect of the present disclosure performs the process shown by FIG.122. The three-dimensional data encoding device calculates coefficientvalues from pieces of attribute information of three-dimensional pointsincluded in point cloud data (S8501); quantizes the coefficient valuesto generate quantization values (S8502); and generates a bitstreamincluding the quantization values (S8503). The three-dimensional pointscorresponding to the coefficient values belong to one layer among one ormore layers. Each of a predetermined number of layers among the one ormore layers is assigned a quantization parameter for the layer. In thequantizing (S8502), (i) when a quantization parameter is assigned to alayer to which each of the coefficient values belongs, the coefficientvalue is quantized using the quantization parameter, and (ii) when thequantization parameter is not assigned to a layer to which each of thecoefficient values belongs, the coefficient value is quantized using aquantization parameter assigned to one layer among the predeterminednumber of the layers. According to the three-dimensional data encodingmethod, the quantization parameter can be changed for each layer, andtherefore the encoding can be properly performed.

For example, the one layer is a last layer among the predeterminednumber of the layers.

For example, in the quantizing (S8502), when a total number of the oneor more layers is less than the predetermined number of the layers,quantization parameters assigned to the predetermined number of thelayers and not corresponding to the one or more layers are not used.

For example, the bitstream includes first information indicating areference quantization parameter, and pieces of second information forcalculating quantization parameters for the one or more layers from thereference quantization parameter. For this reason, it is possible toimprove coding efficiency.

It should be noted that steps S8501, S8502, and S8503 correspond to therespective processes described in Embodiment 9.

For example, the three-dimensional data encoding device includes aprocessor and memory. Using the memory, the processor performs theabove-described process.

A three-dimensional data decoding device according to one aspect of thepresent disclosure performs the process shown by FIG. 123. Thethree-dimensional data decoding device inverse quantizes quantizationvalues to generate coefficient values, the quantization values beingincluded in a bitstream (S8511); and calculates, from the coefficientvalues, pieces of attribute information of three-dimensional pointsincluded in point cloud data (S8512). The three-dimensional pointscorresponding to the coefficient values belong to one layer among one ormore layers. Each of a predetermined number of layers among the one ormore layers is assigned a quantization parameter for the layer. In theinverse quantizing (S8511), (i) when a quantization parameter isassigned to a layer to which each of the quantization values belongs,the quantization value is inverse quantized using the quantizationparameter, and (ii) when the quantization parameter is not assigned to alayer to which each of the quantization values belongs, the quantizationvalue is inverse quantized using a quantization parameter assigned toone layer among the predetermined number of the layers. According to thethree-dimensional data decoding method, the quantization parameter canbe changed for each layer, and therefore the decoding can be properlyperformed.

For example, the one layer is a last layer among the predeterminednumber of the layers.

For example, in the inverse quantizing, when a total number of the oneor more layers is less than the predetermined number of the layers,quantization parameters assigned to the predetermined number of thelayers and not corresponding to the one or more layers are not used.

For example, the bitstream includes first information indicating areference quantization parameter, and pieces of second information forcalculating quantization parameters for the one or more layers from thereference quantization parameter. For this reason, a bitstream whosecoding efficiency has been improved can be decoded appropriately.

It should be noted that steps S8511, S8512, and S8513 correspond to therespective processes described in Embodiment 9.

For example, the three-dimensional data decoding device includes aprocessor and memory. Using the memory, the processor performs theabove-described process.

A three-dimensional data encoding device according to another aspect ofthe present disclosure may perform steps S8501, S8502, and S8503described with reference to FIG. 122, in the following manner. Thethree-dimensional data encoding device calculates coefficient valuesfrom pieces of attribute information of three-dimensional pointsincluded in point cloud data (S8501); quantizes the coefficient valuesto generate quantization values (S8502); and generates a bitstreamincluding the quantization values (S8503). Each of the coefficientvalues belongs to one group among groups that is associated with, amongthree-dimensional spaces, a three-dimensional space to which athree-dimensional point having attribute information used to calculatethe coefficient value belongs. In the quantizing (S8502), each of thecoefficient values is quantized using a quantization parameter for theone group to which the coefficient value belongs. With this, since thethree-dimensional data encoding device can change the quantizationparameter for each group, the three-dimensional data encoding device iscapable of performing encoding appropriately.

For example, the bitstream includes space information indicating, foreach group, a reference point of a three-dimensional space correspondingto the group, and a size of the three-dimensional space corresponding tothe group.

For example, the bitstream includes a flag indicating whether the spaceinformation and a quantization parameter for a group ofthree-dimensional spaces indicated by the space information areincluded.

For example, the three-dimensional data encoding device includes aprocessor and memory. Using the memory, the processor performs theabove-described process.

A three-dimensional data decoding device according to another aspect ofthe present disclosure may performs steps S8511 and S8512 described withreference to FIG. 123, in the following manner. The three-dimensionaldata decoding device inverse quantizes quantization values to generatecoefficient values, the quantization values being included in abitstream (S8511); and calculates, from the coefficient values, piecesof attribute information of three-dimensional points included in pointcloud data (S8512). Each of the quantization values belongs to one groupamong groups that is associated with, among three-dimensional spaces, athree-dimensional space to which a three-dimensional point havingattribute information used to calculate the quantization value belongs.Each of a predetermined number of layers among the one or more layers isassigned a quantization parameter for the layer. In the inversequantizing, each of the quantization values is inverse quantized using aquantization parameter for a layer to which the quantization valuebelongs.

For example, the bitstream includes space information indicating, foreach group, a reference point of a three-dimensional space correspondingto the group, and a size of the three-dimensional space corresponding tothe group.

For example, the bitstream includes a flag indicating whether the spaceinformation and a quantization parameter for a group ofthree-dimensional spaces indicated by the space information areincluded.

For example, the three-dimensional data decoding device includes aprocessor and memory. Using the memory, the processor performs theabove-described process.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to the embodiments of thepresent disclosure have been described above, but the present disclosureis not limited to these embodiments.

Note that each of the processors included in the three-dimensional dataencoding device, the three-dimensional data decoding device, and thelike according to the above embodiments is typically implemented as alarge-scale integrated (LSI) circuit, which is an integrated circuit(IC). These may take the form of individual chips, or may be partiallyor entirely packaged into a single chip.

Such IC is not limited to an LSI, and thus may be implemented as adedicated circuit or a general-purpose processor. Alternatively, a fieldprogrammable gate array (FPGA) that allows for programming after themanufacture of an LSI, or a reconfigurable processor that allows forreconfiguration of the connection and the setting of circuit cellsinside an LSI may be employed.

Moreover, in the above embodiments, the structural components may beimplemented as dedicated hardware or may be realized by executing asoftware program suited to such structural components. Alternatively,the structural components may be implemented by a program executor suchas a CPU or a processor reading out and executing the software programrecorded in a recording medium such as a hard disk or a semiconductormemory.

The present disclosure may also be implemented as a three-dimensionaldata encoding method, a three-dimensional data decoding method, or thelike executed by the three-dimensional data encoding device, thethree-dimensional data decoding device, and the like.

Also, the divisions of the functional blocks shown in the block diagramsare mere examples, and thus a plurality of functional blocks may beimplemented as a single functional block, or a single functional blockmay be divided into a plurality of functional blocks, or one or morefunctions may be moved to another functional block. Also, the functionsof a plurality of functional blocks having similar functions may beprocessed by single hardware or software in a parallelized ortime-divided manner.

Also, the processing order of executing the steps shown in theflowcharts is a mere illustration for specifically describing thepresent disclosure, and thus may be an order other than the shown order.Also, one or more of the steps may be executed simultaneously (inparallel) with another step.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to one or more aspects have beendescribed above based on the embodiments, but the present disclosure isnot limited to these embodiments. The one or more aspects may thusinclude forms achieved by making various modifications to the aboveembodiments that can be conceived by those skilled in the art, as wellforms achieved by combining structural components in differentembodiments, without materially departing from the spirit of the presentdisclosure.

Although only some exemplary embodiments of the present disclosure havebeen described in detail above, those skilled in the art will readilyappreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of the present disclosure. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure.

INDUSTRIAL APPLICABILITY

The present disclosure is applicable to a three-dimensional dataencoding device and a three-dimensional data decoding device.

What is claimed is:
 1. A three-dimensional data encoding method,comprising: calculating coefficient values from pieces of attributeinformation of three-dimensional points included in point cloud data;quantizing the coefficient values to generate quantization values; andgenerating a bitstream including the quantization values, wherein thethree-dimensional points corresponding to the coefficient values belongto one layer among one or more layers, each of a predetermined number oflayers among the one or more layers is assigned a quantization parameterfor the layer, and in the quantizing, (i) when a quantization parameteris assigned to a layer to which each of the coefficient values belongs,the coefficient value is quantized using the quantization parameter, and(ii) when the quantization parameter is not assigned to a layer to whicheach of the coefficient values belongs, the coefficient value isquantized using a quantization parameter assigned to one layer among thepredetermined number of the layers.
 2. The three-dimensional dataencoding method according to claim 1, wherein the one layer is a lastlayer among the predetermined number of the layers.
 3. Thethree-dimensional data encoding method according to claim 1, wherein inthe quantizing, when a total number of the one or more layers is lessthan the predetermined number of the layers, quantization parametersassigned to the predetermined number of the layers and not correspondingto the one or more layers are not used.
 4. The three-dimensional dataencoding method according to claim 1, wherein the bitstream includesfirst information indicating a reference quantization parameter, andpieces of second information for calculating quantization parameters forthe one or more layers from the reference quantization parameter.
 5. Athree-dimensional data decoding method, comprising: inverse quantizingquantization values to generate coefficient values, the quantizationvalues being included in a bitstream; and calculating, from thecoefficient values, pieces of attribute information of three-dimensionalpoints included in point cloud data, wherein the three-dimensionalpoints corresponding to the coefficient values belong to one layer amongone or more layers, each of a predetermined number of layers among theone or more layers is assigned a quantization parameter for the layer,and in the inverse quantizing, (i) when a quantization parameter isassigned to a layer to which each of the quantization values belongs,the quantization value is inverse quantized using the quantizationparameter, and (ii) when the quantization parameter is not assigned to alayer to which each of the quantization values belongs, the quantizationvalue is inverse quantized using a quantization parameter assigned toone layer among the predetermined number of the layers.
 6. Thethree-dimensional data decoding method according to claim 5, wherein theone layer is a last layer among the predetermined number of the layers.7. The three-dimensional data decoding method according to claim 5,wherein in the inverse quantizing, when a total number of the one ormore layers is less than the predetermined number of the layers,quantization parameters assigned to the predetermined number of thelayers and not corresponding to the one or more layers are not used. 8.The three-dimensional data decoding method according to claim 5, whereinthe bitstream includes first information indicating a referencequantization parameter, and pieces of second information for calculatingquantization parameters for the one or more layers from the referencequantization parameter.
 9. A three-dimensional data encoding device,comprising: a processor; and memory, wherein using the memory, theprocessor: calculates coefficient values from pieces of attributeinformation of three-dimensional points included in point cloud data;quantizes the coefficient values to generate quantization values; andgenerates a bitstream including the quantization values, wherein thethree-dimensional points corresponding to the coefficient values belongto one layer among one or more layers, each of a predetermined number oflayers among the one or more layers is assigned a quantization parameterfor the layer, and in the quantizing, (i) when a quantization parameteris assigned to a layer to which each of the coefficient values belongs,the coefficient value is quantized using the quantization parameter, and(ii) when the quantization parameter is not assigned to a layer to whicheach of the coefficient values belongs, the coefficient value isquantized using a quantization parameter assigned to one layer among thepredetermined number of the layers.
 10. A three-dimensional datadecoding device, comprising: a processor; and memory, wherein using thememory, the processor: inverse quantizes quantization values to generatecoefficient values, the quantization values being included in abitstream; and calculates, from the coefficient values, pieces ofattribute information of three-dimensional points included in pointcloud data, wherein the three-dimensional points corresponding to thecoefficient values belong to one layer among one or more layers, each ofa predetermined number of layers among the one or more layers isassigned a quantization parameter for the layer, and in the inversequantizing, (i) when a quantization parameter is assigned to a layer towhich each of the quantization values belongs, the quantization value isinverse quantized using the quantization parameter, and (ii) when thequantization parameter is not assigned to a layer to which each of thequantization values belongs, the quantization value is inverse quantizedusing a quantization parameter assigned to one layer among thepredetermined number of the layers.